Low-latency intelligent automated assistant

ABSTRACT

Systems and processes for operating a digital assistant are provided. In an example process, low-latency operation of a digital assistant is provided. In this example, natural language processing, task flow processing, dialogue flow processing, speech synthesis, or any combination thereof can be at least partially performed while awaiting detection of a speech end-point condition. Upon detection of a speech end-point condition, results obtained from performing the operations can be presented to the user. In another example, robust operation of a digital assistant is provided. In this example, task flow processing by the digital assistant can include selecting a candidate task flow from a plurality of candidate task flows based on determined task flow scores. The task flow scores can be based on speech recognition confidence scores, intent confidence scores, flow parameter scores, or any combination thereof. The selected candidate task flow is executed and corresponding results presented to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/679,595, filed on Aug. 17, 2017, entitled “LOW-LATENCY INTELLIGENTAUTOMATED ASSISTANT,” which claims priority from U.S. Provisional Ser.No. 62/505,546, filed on May 12, 2017, entitled “LOW-LATENCY INTELLIGENTAUTOMATED ASSISTANT,” which are hereby incorporated by reference intheir entirety for all purposes.

FIELD

This relates generally to intelligent automated assistants and, morespecifically, to low-latency intelligent automated assistants.

BACKGROUND

Intelligent automated assistants (or digital assistants) can provide abeneficial interface between human users and electronic devices. Suchassistants can allow users to interact with devices or systems usingnatural language in spoken and/or text forms. For example, a user canprovide a speech input containing a user request to a digital assistantoperating on an electronic device. The digital assistant can interpretthe user's intent from the speech input and operationalize the user'sintent into tasks. The tasks can then be performed by executing one ormore services of the electronic device, and a relevant output responsiveto the user request can be returned to the user.

Digital assistants are frequently implemented on mobile computingplatforms, such as smart phones and tablet computers. However, suchmobile computing platforms can have limited computing resources (e.g.,memory and processor power) and thus digital assistants implemented onsuch platforms can suffer from longer processing times and thus greaterlatency in responding to user requests. This can result in poor userexperience, which can limit the widespread adoption of digitalassistants on mobile platforms.

SUMMARY

Systems and processes for operating a digital assistant are provided. Inone example process, low-latency operation of a digital assistant isprovided. In this example, a stream of audio is received. In particular,a first portion of the stream of audio containing a user utterance isreceived from a first time to a second time and a second portion of thestream of audio is received from the second time to a third time. Theprocess determines whether the first portion of the stream of audiosatisfies a predetermined condition. In response to determining that thefirst portion of the stream of audio satisfies a predeterminedcondition, operations are at least partially performed between thesecond time and the third time. The operations include determining,based on one or more candidate text representations of the userutterance, a plurality of candidate user intents for the user utterance.Each candidate user intent of the plurality of candidate user intentscorresponds to a respective candidate task flow of a plurality ofcandidate task flows. The operations also include selecting a firstcandidate task flow of the plurality of candidate task flows. Inaddition, the operations include executing the first candidate task flowwithout providing an output to a user of the device. The processdetermines whether a speech end-point condition is detected between thesecond time and the third time. In response to determining that a speechend-point condition is detected between the second time and the thirdtime, results from executing the selected first candidate task flow arepresented to the user.

Performing, at least partially between the second time and the thirdtime, and in response to determining that the first portion of thestream of audio satisfies a predetermined condition, operations thatinclude determining a plurality of candidate user intents, selecting afirst candidate task flow, and executing the first candidate task flowcan enable the electronic device to at least partially complete theseoperations while waiting for the speech end-point condition to bedetected. This can enhance operability of the electronic device byreducing the amount of computation needed to be performed afterdetecting the speech end-point condition, which in turn can reduce theoverall latency between receiving the user utterance and presenting theresults to the user.

In another example process for low-latency operation of a digitalassistant, a stream of audio is received. In particular, a first portionof the stream of audio containing a user utterance is received from afirst time to a second time and a second portion of the stream of audiois received from the second time to a third time. The process determineswhether the first portion of the stream of audio satisfies apredetermined condition. In response to determining that the firstportion of the stream of audio satisfies a predetermined condition,operations are at least partially performed between the second time andthe third time. The operations include causing generation of a textdialogue that is responsive to the user utterance. The operations alsoinclude determining whether the memory of the device stores an audiofile having a spoken representation of the text dialogue. In response todetermining that the memory of the device does not store an audio filehaving a spoken representation of the text dialogue, the operationsinclude generating an audio file having a spoken representation of thetext dialogue and storing the audio file in the memory. The processdetermines whether a speech end-point condition is detected between thesecond time and the third time. In response to determining that a speechend-point condition is detected between the second time and the thirdtime, the spoken representation of the text dialogue is outputted to auser of the device by playing the stored audio file.

Performing, at least partially between the second time and the thirdtime and in response to determining that the first portion of the streamof audio satisfies a predetermined condition, operations that includecausing generation of a text dialogue and generating an audio filehaving a spoken representation of the text dialogue can enable theelectronic device to at least partially complete these operations whilewaiting for the speech end-point condition to be detected. This canenhance operability of the electronic device by reducing the amount ofcomputation needed to be performed after detecting the speech end-pointcondition, which in turn can reduce the overall latency betweenreceiving the user utterance and outputting the spoken representation ofthe text dialogue to the user.

Systems and processes for robust operation of a digital assistant arealso provided. In an example process, a user utterance is received.Based on a plurality of candidate text representations of the userutterance, a plurality of candidate user intents for the user utteranceare determined. Each candidate user intent of the plurality of candidateuser intents corresponds to a respective candidate task flow of aplurality of candidate task flows. A plurality of task flow scores forthe plurality of candidate task flows are determined. Each task flowscore of the plurality of task flow scores corresponds to a respectivecandidate task flow of the plurality of candidate task flows. Based onthe plurality of task flow scores, a first candidate task flow of theplurality of candidate task flows is selected. The first candidate taskflow is executed, including presenting to the user results fromexecuting the first candidate task flow.

Determining a plurality of task flow scores for the plurality ofcandidate task flows and selecting the first candidate task flow basedon the plurality of task flow scores can enable the electronic device toevaluate the reliability and feasibility of each candidate task flowprior to selecting and executing the first candidate task flow. This canenhance operability of the electronic device by improving the likelihoodthat the selected first candidate task flow coincides with the user'sactual desired goal for providing the user utterance. In turn, this canallow the electronic device to operate with greater accuracy andreliability when identifying and performing tasks in response to a userutterance.

Executable instructions for performing the functions described hereinare, optionally, included in a non-transitory computer-readable storagemedium or other computer-program product configured for execution by oneor more processors. Executable instructions for performing thesefunctions are, optionally, included in a transitory computer-readablestorage medium or other computer program product configured forexecution by one or more processors.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a system and environment forimplementing a digital assistant, according to various examples.

FIG. 2A is a block diagram illustrating a portable multifunction deviceimplementing the client-side portion of a digital assistant, accordingto various examples.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling, according to various examples.

FIG. 3 illustrates a portable multifunction device implementing theclient-side portion of a digital assistant, according to variousexamples.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface, according to various examples.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on a portable multifunction device, according to variousexamples.

FIG. 5B illustrates an exemplary user interface for a multifunctiondevice with a touch-sensitive surface that is separate from the display,according to various examples.

FIG. 6A illustrates a personal electronic device, according to variousexamples.

FIG. 6B is a block diagram illustrating a personal electronic device,according to various examples.

FIG. 7A is a block diagram illustrating a digital assistant system or aserver portion thereof, according to various examples.

FIG. 7B illustrates the functions of the digital assistant shown in FIG.7A, according to various examples.

FIG. 7C illustrates a portion of an ontology, according to variousexamples.

FIG. 8 is a block diagram illustrating a portion of a digital assistantmodule, according to various examples.

FIG. 9 is a timeline illustrating the timing of low-latency operation ofa digital assistant, according to various examples.

FIG. 10 is a timeline illustrating the timing of low-latency operationof a digital assistant, according to various examples.

FIGS. 11A-11B illustrate a process for operating a digital assistant,according to various examples.

FIG. 12 illustrates a process for operating a digital assistant togenerate a spoken dialogue response, according to various examples.

DETAILED DESCRIPTION

In the following description of examples, reference is made to theaccompanying drawings in which are shown by way of illustration specificexamples that can be practiced. It is to be understood that otherexamples can be used and structural changes can be made withoutdeparting from the scope of the various examples.

As discussed above, digital assistants implemented on mobile computingplatforms can suffer from longer processing times and thus greaterlatency in responding to user requests. In particular, certain processesperformed by digital assistants, such as natural language processing,task flow processing, and/or speech synthesis, can be computationallyintensive and contribute significantly to the response latency. In somedigital assistant systems, the aforementioned processes are initiatedonly after a speech end-point condition is detected. Detecting a speechend-point condition establishes that the user has finished providinghis/her spoken request. However, a speech end-point condition isfrequently detected based on an absence of user speech for greater thana predetermined duration (e.g., 600 ms, 700 ms, or 800 ms). This meansthat the total latency experienced by the user after the user finishesproviding his/her spoken request can include the predetermined durationneeded to detect a speech end-point condition and the computational timerequired for the digital assistant system to process the spoken request(e.g., by performing natural language processing, task flow processing,and/or speech synthesis). Given the limited computational resources ofmobile computing platforms, this total latency can be considerableenough to significantly impact user engagement. It can thus be desirableto reduce the total latency experienced by the user from the time theuser finishes providing his/her spoken request to the time the digitalassistant system presents a response to the user request.

Techniques for reducing response latency for digital assistant systemsare described herein. In particular, in some exemplary processes,natural language processing, task flow processing, and/or speechsynthesis can be initiated during the predetermined duration needed todetect a speech end-point condition. For instance, in a specificexample, natural language processing, task flow processing, and/orspeech synthesis can be initiated upon detecting a short pause (e.g., 50ms, 75 ms, or 100 ms) in user speech. If the short pause develops into along pause (e.g., 600 ms, 700 ms, or 800 ms) that corresponds to aspeech end-point condition, natural language processing, task flowprocessing, and/or speech synthesis would be at least partiallycompleted at the time the speech end-point condition is detected. Thiscan result in a reduction in the total latency experienced by the user.

In one example process for reducing response latency in a digitalassistant system, a stream of audio is received. In particular, a firstportion of the stream of audio containing a user utterance is receivedfrom a first time to a second time and a second portion of the stream ofaudio is received from the second time to a third time. The processdetermines whether the first portion of the stream of audio satisfies apredetermined condition. In response to determining that the firstportion of the stream of audio satisfies a predetermined condition,operations are at least partially performed between the second time andthe third time. The operations include determining, based on one or morecandidate text representations of the user utterance, a plurality ofcandidate user intents for the user utterance. Each candidate userintent of the plurality of candidate user intents corresponds to arespective candidate task flow of a plurality of candidate task flows.The operations also include selecting a first candidate task flow of theplurality of candidate task flows. In addition, the operations includeexecuting the first candidate task flow without providing an output to auser of the device. In some examples, executing the first candidate taskflow includes generating spoken dialogue that is responsive to the userutterance without outputting the generated spoken dialogue. The processdetermines whether a speech end-point condition is detected between thesecond time and the third time. In response to determining that a speechend-point condition is detected between the second time and the thirdtime, results from executing the selected first candidate task flow arepresented to the user. In some examples, presenting the results includesoutputting the generated spoken dialogue.

Techniques for robust operation of a digital assistant are alsodescribed herein. In particular, due to the inherent ambiguity in humanspeech, there is inherent uncertainty during speech recognition andnatural language processing of human speech. As a result, speechrecognition and natural language processing errors can frequently occurwhen digital assistant systems process spoken user requests. Sucherrors, when propagated through task flow processing, can at timesresult in fatal errors (e.g., no response) or in the performance oftasks that do not correspond to the user's desired goal.

An illustrative example of a task flow processing error caused by aspeech recognition error is provided. In this example, the user providesthe spoken request “What are Mike Dunleavy's stats?” During speechrecognition processing, the digital assistant system can erroneouslytranscribe the spoken request as “What are Mike Dunleavey's stats?”Subsequently, during natural language processing, the digital assistantsystem can recognize (e.g., based on the word “stats”) that the user isrequesting for sports information and that “Mike Dunleavey” is asports-related entity. Based on this interpretation, the digitalassistant system can perform task flow processing and select a task flowthat includes procedures for searching sports-related data sources for“Mike Dunleavey.” However, during execution of the selected task flow,the digital assistant system may be unable to locate any informationrelated to “Mike Dunleavey” in the sports-related sources due to thespeech recognition error. As a result, the digital assistant system canfail to provide any substantive response to the user's request.

In another illustrative example of a task flow processing error, theuser can provide the spoken request “Directions to FidelityInvestments.” In this example, the digital assistant system cansuccessfully transcribe the spoken request as “Directions to FidelityInvestments.” During subsequent natural language processing, the digitalassistant system can recognize (e.g., based on the word “directions”)that the user is requesting for directions. However, rather thaninterpreting “Fidelity Investments” as a business, the digital assistantsystem can erroneously interpret “Fidelity Investments” as a person inthe user's contact list (e.g., based on the existence of an entrycorresponding to “Fidelity Investments” in the user's contact list).Based on this erroneous interpretation, the digital assistant system canperform task flow processing and select a task flow that includesprocedures for searching the user's contact list for an addresscorresponding to “Fidelity Investments” and obtaining directions to thataddress. However, during execution of the selected task flow, thedigital assistant system may be unable to find any address correspondingto “Fidelity Investments” in the user's contact list. Specifically,although the user has an entry corresponding to “Fidelity Investments”in his/her contact list, the entry may only include phone numberinformation, but not address information. As a result, the digitalassistant system can fail to provide any substantive response to theuser's request.

Based on the illustrative examples described above, a digital assistantsystem that implements more robust task flow processing can bedesirable. In accordance with some techniques described herein, multiplecandidate task flows associated with multiple candidate user intents canbe evaluated for reliability prior to selecting and executing aparticular task flow. The evaluation process can be based on task flowscores determined for every candidate task flow. The task flow score fora respective candidate task flow can be based on, for example, a speechrecognition confidence score of a respective speech recognition result,an intent confidence score of a respective natural language processingresult, a flow parameter score of the respective candidate task flow, orany combination thereof. In some examples, the flow parameter score canbe based on whether one or more missing flow parameters for therespective candidate task flow can be resolved. For example, referringto the above illustrative examples, the flow parameter score can bebased on whether missing flow parameters (e.g., “sports entity” and“address” flow parameters) associated with “Mike Dunleavey” and“Fidelity Investments” can be resolved. In these examples, the flowparameter scores can be low because the missing flow parameters cannotbe resolved. The digital assistant system can select a suitablecandidate task flow based on the task flow scores of the candidate taskflows. For example, a candidate task flow having a task flow score thatis maximized based on the combined speech recognition confidence score,intent confidence score, and flow parameter score can be selected. Byselecting a suitable candidate task flow based on determined task flowscores for every candidate task flow, the selected candidate task flowcan be more likely to coincide with the user's intended goal. Moreover,fatal error may be less likely to occur during execution of a selectedcandidate task flow.

In an example process for robust operation of a digital assistant, auser utterance is received. Based on a plurality of candidate textrepresentations of the user utterance, a plurality of candidate userintents for the user utterance are determined. Each candidate userintent of the plurality of candidate user intents corresponds to arespective candidate task flow of a plurality of candidate task flows. Aplurality of task flow scores for the plurality of candidate task flowsare determined. Each task flow score of the plurality of task flowscores corresponds to a respective candidate task flow of the pluralityof candidate task flows. Based on the plurality of task flow scores, afirst candidate task flow of the plurality of candidate task flows isselected. The first candidate task flow is executed, includingpresenting, to the user, results from executing the first candidate taskflow.

Although the following description uses the terms “first,” “second,”etc. to describe various elements, these elements should not be limitedby the terms. These terms are only used to distinguish one element fromanother. For example, a first input could be termed a second input, and,similarly, a second input could be termed a first input, withoutdeparting from the scope of the various described examples. The firstinput and the second input are both inputs and, in some cases, areseparate and different inputs.

The terminology used in the description of the various describedexamples herein is for the purpose of describing particular examplesonly and is not intended to be limiting. As used in the description ofthe various described examples and the appended claims, the singularforms “a,” “an,” and “the” are intended to include the plural forms aswell, unless the context clearly indicates otherwise. It will also beunderstood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“includes,” “including,” “comprises,” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The term “if” may be construed to mean “when” or “upon” or “in responseto determining” or “in response to detecting,” depending on the context.Similarly, the phrase “if it is determined” or “if [a stated conditionor event] is detected” may be construed to mean “upon determining” or“in response to determining” or “upon detecting [the stated condition orevent]” or “in response to detecting [the stated condition or event],”depending on the context.

1. System and Environment

FIG. 1 illustrates a block diagram of system 100 according to variousexamples. In some examples, system 100 implements a digital assistant.The terms “digital assistant,” “virtual assistant,” “intelligentautomated assistant,” or “automatic digital assistant” refer to anyinformation processing system that interprets natural language input inspoken and/or textual form to infer user intent, and performs actionsbased on the inferred user intent. For example, to act on an inferreduser intent, the system performs one or more of the following:identifying a task flow with steps and parameters designed to accomplishthe inferred user intent, inputting specific requirements from theinferred user intent into the task flow; executing the task flow byinvoking programs, methods, services, APIs, or the like; and generatingoutput responses to the user in an audible (e.g., speech) and/or visualform.

Specifically, a digital assistant is capable of accepting a user requestat least partially in the form of a natural language command, request,statement, narrative, and/or inquiry. Typically, the user request seekseither an informational answer or performance of a task by the digitalassistant. A satisfactory response to the user request includes aprovision of the requested informational answer, a performance of therequested task, or a combination of the two. For example, a user asksthe digital assistant a question, such as “Where am I right now?” Basedon the user's current location, the digital assistant answers, “You arein Central Park near the west gate.” The user also requests theperformance of a task, for example, “Please invite my friends to mygirlfriend's birthday party next week.” In response, the digitalassistant can acknowledge the request by saying “Yes, right away,” andthen send a suitable calendar invite on behalf of the user to each ofthe user's friends listed in the user's electronic address book. Duringperformance of a requested task, the digital assistant sometimesinteracts with the user in a continuous dialogue involving multipleexchanges of information over an extended period of time. There arenumerous other ways of interacting with a digital assistant to requestinformation or performance of various tasks. In addition to providingverbal responses and taking programmed actions, the digital assistantalso provides responses in other visual or audio forms, e.g., as text,alerts, music, videos, animations, etc.

As shown in FIG. 1, in some examples, a digital assistant is implementedaccording to a client-server model. The digital assistant includesclient-side portion 102 (hereafter “DA client 102”) executed on userdevice 104 and server-side portion 106 (hereafter “DA server 106”)executed on server system 108. DA client 102 communicates with DA server106 through one or more networks 110. DA client 102 provides client-sidefunctionalities such as user-facing input and output processing andcommunication with DA server 106. DA server 106 provides server-sidefunctionalities for any number of DA clients 102 each residing on arespective user device 104.

In some examples, DA server 106 includes client-facing I/O interface112, one or more processing modules 114, data and models 116, and I/Ointerface to external services 118. The client-facing I/O interface 112facilitates the client-facing input and output processing for DA server106. One or more processing modules 114 utilize data and models 116 toprocess speech input and determine the user's intent based on naturallanguage input. Further, one or more processing modules 114 perform taskexecution based on inferred user intent. In some examples, DA server 106communicates with external services 120 through network(s) 110 for taskcompletion or information acquisition. I/O interface to externalservices 118 facilitates such communications.

User device 104 can be any suitable electronic device. In some examples,user device is a portable multifunctional device (e.g., device 200,described below with reference to FIG. 2A), a multifunctional device(e.g., device 400, described below with reference to FIG. 4), or apersonal electronic device (e.g., device 600, described below withreference to FIG. 6A-B.) A portable multifunctional device is, forexample, a mobile telephone that also contains other functions, such asPDA and/or music player functions. Specific examples of portablemultifunction devices include the iPhone®, iPod Touch®, and iPad®devices from Apple Inc. of Cupertino, Calif. Other examples of portablemultifunction devices include, without limitation, laptop or tabletcomputers. Further, in some examples, user device 104 is a non-portablemultifunctional device. In particular, user device 104 is a desktopcomputer, a game console, a television, or a television set-top box. Insome examples, user device 104 includes a touch-sensitive surface (e.g.,touch screen displays and/or touchpads). Further, user device 104optionally includes one or more other physical user-interface devices,such as a physical keyboard, a mouse, and/or a joystick. Variousexamples of electronic devices, such as multifunctional devices, aredescribed below in greater detail.

Examples of communication network(s) 110 include local area networks(LAN) and wide area networks (WAN), e.g., the Internet. Communicationnetwork(s) 110 is implemented using any known network protocol,including various wired or wireless protocols, such as, for example,Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), codedivision multiple access (CDMA), time division multiple access (TDMA),Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or anyother suitable communication protocol.

Server system 108 is implemented on one or more standalone dataprocessing apparatus or a distributed network of computers. In someexamples, server system 108 also employs various virtual devices and/orservices of third-party service providers (e.g., third-party cloudservice providers) to provide the underlying computing resources and/orinfrastructure resources of server system 108.

In some examples, user device 104 communicates with DA server 106 viasecond user device 122. Second user device 122 is similar or identicalto user device 104. For example, second user device 122 is similar todevices 200, 400, or 600 described below with reference to FIGS. 2A, 4,and 6A-B. User device 104 is configured to communicatively couple tosecond user device 122 via a direct communication connection, such asBluetooth, NFC, BTLE, or the like, or via a wired or wireless network,such as a local Wi-Fi network. In some examples, second user device 122is configured to act as a proxy between user device 104 and DA server106. For example, DA client 102 of user device 104 is configured totransmit information (e.g., a user request received at user device 104)to DA server 106 via second user device 122. DA server 106 processes theinformation and return relevant data (e.g., data content responsive tothe user request) to user device 104 via second user device 122.

In some examples, user device 104 is configured to communicateabbreviated requests for data to second user device 122 to reduce theamount of information transmitted from user device 104. Second userdevice 122 is configured to determine supplemental information to add tothe abbreviated request to generate a complete request to transmit to DAserver 106. This system architecture can advantageously allow userdevice 104 having limited communication capabilities and/or limitedbattery power (e.g., a watch or a similar compact electronic device) toaccess services provided by DA server 106 by using second user device122, having greater communication capabilities and/or battery power(e.g., a mobile phone, laptop computer, tablet computer, or the like),as a proxy to DA server 106. While only two user devices 104 and 122 areshown in FIG. 1, it should be appreciated that system 100, in someexamples, includes any number and type of user devices configured inthis proxy configuration to communicate with DA server system 106.

Although the digital assistant shown in FIG. 1 includes both aclient-side portion (e.g., DA client 102) and a server-side portion(e.g., DA server 106), in some examples, the functions of a digitalassistant are implemented as a standalone application installed on auser device. In addition, the divisions of functionalities between theclient and server portions of the digital assistant can vary indifferent implementations. For instance, in some examples, the DA clientis a thin-client that provides only user-facing input and outputprocessing functions, and delegates all other functionalities of thedigital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices forimplementing the client-side portion of a digital assistant. FIG. 2A isa block diagram illustrating portable multifunction device 200 withtouch-sensitive display system 212 in accordance with some embodiments.Touch-sensitive display 212 is sometimes called a “touch screen” forconvenience and is sometimes known as or called a “touch-sensitivedisplay system.” Device 200 includes memory 202 (which optionallyincludes one or more computer-readable storage mediums), memorycontroller 222, one or more processing units (CPUs) 220, peripheralsinterface 218, RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, input/output (I/O) subsystem 206, other input controldevices 216, and external port 224. Device 200 optionally includes oneor more optical sensors 264. Device 200 optionally includes one or morecontact intensity sensors 265 for detecting intensity of contacts ondevice 200 (e.g., a touch-sensitive surface such as touch-sensitivedisplay system 212 of device 200). Device 200 optionally includes one ormore tactile output generators 267 for generating tactile outputs ondevice 200 (e.g., generating tactile outputs on a touch-sensitivesurface such as touch-sensitive display system 212 of device 200 ortouchpad 455 of device 400). These components optionally communicateover one or more communication buses or signal lines 203.

As used in the specification and claims, the term “intensity” of acontact on a touch-sensitive surface refers to the force or pressure(force per unit area) of a contact (e.g., a finger contact) on thetouch-sensitive surface, or to a substitute (proxy) for the force orpressure of a contact on the touch-sensitive surface. The intensity of acontact has a range of values that includes at least four distinctvalues and more typically includes hundreds of distinct values (e.g., atleast 256). Intensity of a contact is, optionally, determined (ormeasured) using various approaches and various sensors or combinationsof sensors. For example, one or more force sensors underneath oradjacent to the touch-sensitive surface are, optionally, used to measureforce at various points on the touch-sensitive surface. In someimplementations, force measurements from multiple force sensors arecombined (e.g., a weighted average) to determine an estimated force of acontact. Similarly, a pressure-sensitive tip of a stylus is, optionally,used to determine a pressure of the stylus on the touch-sensitivesurface. Alternatively, the size of the contact area detected on thetouch-sensitive surface and/or changes thereto, the capacitance of thetouch-sensitive surface proximate to the contact and/or changes thereto,and/or the resistance of the touch-sensitive surface proximate to thecontact and/or changes thereto are, optionally, used as a substitute forthe force or pressure of the contact on the touch-sensitive surface. Insome implementations, the substitute measurements for contact force orpressure are used directly to determine whether an intensity thresholdhas been exceeded (e.g., the intensity threshold is described in unitscorresponding to the substitute measurements). In some implementations,the substitute measurements for contact force or pressure are convertedto an estimated force or pressure, and the estimated force or pressureis used to determine whether an intensity threshold has been exceeded(e.g., the intensity threshold is a pressure threshold measured in unitsof pressure). Using the intensity of a contact as an attribute of a userinput allows for user access to additional device functionality that mayotherwise not be accessible by the user on a reduced-size device withlimited real estate for displaying affordances (e.g., on atouch-sensitive display) and/or receiving user input (e.g., via atouch-sensitive display, a touch-sensitive surface, or aphysical/mechanical control such as a knob or a button).

As used in the specification and claims, the term “tactile output”refers to physical displacement of a device relative to a previousposition of the device, physical displacement of a component (e.g., atouch-sensitive surface) of a device relative to another component(e.g., housing) of the device, or displacement of the component relativeto a center of mass of the device that will be detected by a user withthe user's sense of touch. For example, in situations where the deviceor the component of the device is in contact with a surface of a userthat is sensitive to touch (e.g., a finger, palm, or other part of auser's hand), the tactile output generated by the physical displacementwill be interpreted by the user as a tactile sensation corresponding toa perceived change in physical characteristics of the device or thecomponent of the device. For example, movement of a touch-sensitivesurface (e.g., a touch-sensitive display or trackpad) is, optionally,interpreted by the user as a “down click” or “up click” of a physicalactuator button. In some cases, a user will feel a tactile sensationsuch as an “down click” or “up click” even when there is no movement ofa physical actuator button associated with the touch-sensitive surfacethat is physically pressed (e.g., displaced) by the user's movements. Asanother example, movement of the touch-sensitive surface is, optionally,interpreted or sensed by the user as “roughness” of the touch-sensitivesurface, even when there is no change in smoothness of thetouch-sensitive surface. While such interpretations of touch by a userwill be subject to the individualized sensory perceptions of the user,there are many sensory perceptions of touch that are common to a largemajority of users. Thus, when a tactile output is described ascorresponding to a particular sensory perception of a user (e.g., an “upclick,” a “down click,” “roughness”), unless otherwise stated, thegenerated tactile output corresponds to physical displacement of thedevice or a component thereof that will generate the described sensoryperception for a typical (or average) user.

It should be appreciated that device 200 is only one example of aportable multifunction device, and that device 200 optionally has moreor fewer components than shown, optionally combines two or morecomponents, or optionally has a different configuration or arrangementof the components. The various components shown in FIG. 2A areimplemented in hardware, software, or a combination of both hardware andsoftware, including one or more signal processing and/orapplication-specific integrated circuits.

Memory 202 includes one or more computer-readable storage mediums. Thecomputer-readable storage mediums are, for example, tangible andnon-transitory. Memory 202 includes high-speed random access memory andalso includes non-volatile memory, such as one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices. Memory controller 222 controls access to memory 202 byother components of device 200.

In some examples, a non-transitory computer-readable storage medium ofmemory 202 is used to store instructions (e.g., for performing aspectsof processes described below) for use by or in connection with aninstruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device and execute the instructions. In other examples,the instructions (e.g., for performing aspects of the processesdescribed below) are stored on a non-transitory computer-readablestorage medium (not shown) of the server system 108 or are dividedbetween the non-transitory computer-readable storage medium of memory202 and the non-transitory computer-readable storage medium of serversystem 108.

Peripherals interface 218 is used to couple input and output peripheralsof the device to CPU 220 and memory 202. The one or more processors 220run or execute various software programs and/or sets of instructionsstored in memory 202 to perform various functions for device 200 and toprocess data. In some embodiments, peripherals interface 218, CPU 220,and memory controller 222 are implemented on a single chip, such as chip204. In some other embodiments, they are implemented on separate chips.

RF (radio frequency) circuitry 208 receives and sends RF signals, alsocalled electromagnetic signals. RF circuitry 208 converts electricalsignals to/from electromagnetic signals and communicates withcommunications networks and other communications devices via theelectromagnetic signals. RF circuitry 208 optionally includes well-knowncircuitry for performing these functions, including but not limited toan antenna system, an RF transceiver, one or more amplifiers, a tuner,one or more oscillators, a digital signal processor, a CODEC chipset, asubscriber identity module (SIM) card, memory, and so forth. RFcircuitry 208 optionally communicates with networks, such as theInternet, also referred to as the World Wide Web (WWW), an intranetand/or a wireless network, such as a cellular telephone network, awireless local area network (LAN) and/or a metropolitan area network(MAN), and other devices by wireless communication. The RF circuitry 208optionally includes well-known circuitry for detecting near fieldcommunication (NFC) fields, such as by a short-range communicationradio. The wireless communication optionally uses any of a plurality ofcommunications standards, protocols, and technologies, including but notlimited to Global System for Mobile Communications (GSM), Enhanced DataGSM Environment (EDGE), high-speed downlink packet access (HSDPA),high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO),HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), nearfield communication (NFC), wideband code division multiple access(W-CDMA), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity(Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n,and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, aprotocol for e mail (e.g., Internet message access protocol (IMAP)and/or post office protocol (POP)), instant messaging (e.g., extensiblemessaging and presence protocol (XMPP), Session Initiation Protocol forInstant Messaging and Presence Leveraging Extensions (SIMPLE), InstantMessaging and Presence Service (IMPS)), and/or Short Message Service(SMS), or any other suitable communication protocol, includingcommunication protocols not yet developed as of the filing date of thisdocument.

Audio circuitry 210, speaker 211, and microphone 213 provide an audiointerface between a user and device 200. Audio circuitry 210 receivesaudio data from peripherals interface 218, converts the audio data to anelectrical signal, and transmits the electrical signal to speaker 211.Speaker 211 converts the electrical signal to human-audible sound waves.Audio circuitry 210 also receives electrical signals converted bymicrophone 213 from sound waves. Audio circuitry 210 converts theelectrical signal to audio data and transmits the audio data toperipherals interface 218 for processing. Audio data are retrieved fromand/or transmitted to memory 202 and/or RF circuitry 208 by peripheralsinterface 218. In some embodiments, audio circuitry 210 also includes aheadset jack (e.g., 312, FIG. 3). The headset jack provides an interfacebetween audio circuitry 210 and removable audio input/outputperipherals, such as output-only headphones or a headset with bothoutput (e.g., a headphone for one or both ears) and input (e.g., amicrophone).

I/O subsystem 206 couples input/output peripherals on device 200, suchas touch screen 212 and other input control devices 216, to peripheralsinterface 218. I/O subsystem 206 optionally includes display controller256, optical sensor controller 258, intensity sensor controller 259,haptic feedback controller 261, and one or more input controllers 260for other input or control devices. The one or more input controllers260 receive/send electrical signals from/to other input control devices216. The other input control devices 216 optionally include physicalbuttons (e.g., push buttons, rocker buttons, etc.), dials, sliderswitches, joysticks, click wheels, and so forth. In some alternateembodiments, input controller(s) 260 are, optionally, coupled to any (ornone) of the following: a keyboard, an infrared port, a USB port, and apointer device such as a mouse. The one or more buttons (e.g., 308, FIG.3) optionally include an up/down button for volume control of speaker211 and/or microphone 213. The one or more buttons optionally include apush button (e.g., 306, FIG. 3).

A quick press of the push button disengages a lock of touch screen 212or begin a process that uses gestures on the touch screen to unlock thedevice, as described in U.S. patent application Ser. No. 11/322,549,“Unlocking a Device by Performing Gestures on an Unlock Image,” filedDec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated byreference in its entirety. A longer press of the push button (e.g., 306)turns power to device 200 on or off. The user is able to customize afunctionality of one or more of the buttons. Touch screen 212 is used toimplement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display 212 provides an input interface and an outputinterface between the device and a user. Display controller 256 receivesand/or sends electrical signals from/to touch screen 212. Touch screen212 displays visual output to the user. The visual output includesgraphics, text, icons, video, and any combination thereof (collectivelytermed “graphics”). In some embodiments, some or all of the visualoutput correspond to user-interface objects.

Touch screen 212 has a touch-sensitive surface, sensor, or set ofsensors that accepts input from the user based on haptic and/or tactilecontact. Touch screen 212 and display controller 256 (along with anyassociated modules and/or sets of instructions in memory 202) detectcontact (and any movement or breaking of the contact) on touch screen212 and convert the detected contact into interaction withuser-interface objects (e.g., one or more soft keys, icons, web pages,or images) that are displayed on touch screen 212. In an exemplaryembodiment, a point of contact between touch screen 212 and the usercorresponds to a finger of the user.

Touch screen 212 uses LCD (liquid crystal display) technology, LPD(light emitting polymer display) technology, or LED (light emittingdiode) technology, although other display technologies may be used inother embodiments. Touch screen 212 and display controller 256 detectcontact and any movement or breaking thereof using any of a plurality oftouch sensing technologies now known or later developed, including butnot limited to capacitive, resistive, infrared, and surface acousticwave technologies, as well as other proximity sensor arrays or otherelements for determining one or more points of contact with touch screen212. In an exemplary embodiment, projected mutual capacitance sensingtechnology is used, such as that found in the iPhone® and iPod Touch®from Apple Inc. of Cupertino, Calif.

A touch-sensitive display in some embodiments of touch screen 212 isanalogous to the multi-touch sensitive touchpads described in thefollowing U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No.6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932(Westerman), and/or U.S. Patent Publication 2002/0015024A1, each ofwhich is hereby incorporated by reference in its entirety. However,touch screen 212 displays visual output from device 200, whereastouch-sensitive touchpads do not provide visual output.

A touch-sensitive display in some embodiments of touch screen 212 is asdescribed in the following applications: (1) U.S. patent applicationSer. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2,2006; (2) U.S. patent application Ser. No. 10/840,862, “MultipointTouchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No.10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30,2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures ForTouch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patentapplication Ser. No. 11/038,590, “Mode-Based Graphical User InterfacesFor Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patentapplication Ser. No. 11/228,758, “Virtual Input Device Placement On ATouch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patentapplication Ser. No. 11/228,700, “Operation Of A Computer With A TouchScreen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser.No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen VirtualKeyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No.11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. Allof these applications are incorporated by reference herein in theirentirety.

Touch screen 212 has, for example, a video resolution in excess of 100dpi. In some embodiments, the touch screen has a video resolution ofapproximately 160 dpi. The user makes contact with touch screen 212using any suitable object or appendage, such as a stylus, a finger, andso forth. In some embodiments, the user interface is designed to workprimarily with finger-based contacts and gestures, which can be lessprecise than stylus-based input due to the larger area of contact of afinger on the touch screen. In some embodiments, the device translatesthe rough finger-based input into a precise pointer/cursor position orcommand for performing the actions desired by the user.

In some embodiments, in addition to the touch screen, device 200includes a touchpad (not shown) for activating or deactivatingparticular functions. In some embodiments, the touchpad is atouch-sensitive area of the device that, unlike the touch screen, doesnot display visual output. The touchpad is a touch-sensitive surfacethat is separate from touch screen 212 or an extension of thetouch-sensitive surface formed by the touch screen.

Device 200 also includes power system 262 for powering the variouscomponents. Power system 262 includes a power management system, one ormore power sources (e.g., battery, alternating current (AC)), arecharging system, a power failure detection circuit, a power converteror inverter, a power status indicator (e.g., a light-emitting diode(LED)) and any other components associated with the generation,management and distribution of power in portable devices.

Device 200 also includes one or more optical sensors 264. FIG. 2A showsan optical sensor coupled to optical sensor controller 258 in I/Osubsystem 206. Optical sensor 264 includes charge-coupled device (CCD)or complementary metal-oxide semiconductor (CMOS) phototransistors.Optical sensor 264 receives light from the environment, projectedthrough one or more lenses, and converts the light to data representingan image. In conjunction with imaging module 243 (also called a cameramodule), optical sensor 264 captures still images or video. In someembodiments, an optical sensor is located on the back of device 200,opposite touch screen display 212 on the front of the device so that thetouch screen display is used as a viewfinder for still and/or videoimage acquisition. In some embodiments, an optical sensor is located onthe front of the device so that the user's image is obtained for videoconferencing while the user views the other video conferenceparticipants on the touch screen display. In some embodiments, theposition of optical sensor 264 can be changed by the user (e.g., byrotating the lens and the sensor in the device housing) so that a singleoptical sensor 264 is used along with the touch screen display for bothvideo conferencing and still and/or video image acquisition.

Device 200 optionally also includes one or more contact intensitysensors 265. FIG. 2A shows a contact intensity sensor coupled tointensity sensor controller 259 in I/O subsystem 206. Contact intensitysensor 265 optionally includes one or more piezoresistive strain gauges,capacitive force sensors, electric force sensors, piezoelectric forcesensors, optical force sensors, capacitive touch-sensitive surfaces, orother intensity sensors (e.g., sensors used to measure the force (orpressure) of a contact on a touch-sensitive surface). Contact intensitysensor 265 receives contact intensity information (e.g., pressureinformation or a proxy for pressure information) from the environment.In some embodiments, at least one contact intensity sensor is collocatedwith, or proximate to, a touch-sensitive surface (e.g., touch-sensitivedisplay system 212). In some embodiments, at least one contact intensitysensor is located on the back of device 200, opposite touch screendisplay 212, which is located on the front of device 200.

Device 200 also includes one or more proximity sensors 266. FIG. 2Ashows proximity sensor 266 coupled to peripherals interface 218.Alternately, proximity sensor 266 is coupled to input controller 260 inI/O subsystem 206. Proximity sensor 266 is performed as described inU.S. patent application Ser. No. 11/241,839, “Proximity Detector InHandheld Device”; Ser. No. 11/240,788, “Proximity Detector In HandheldDevice”; Ser. No. 11/620,702, “Using Ambient Light Sensor To AugmentProximity Sensor Output”; Ser. No. 11/586,862, “Automated Response ToAnd Sensing Of User Activity In Portable Devices”; and Ser. No.11/638,251, “Methods And Systems For Automatic Configuration OfPeripherals,” which are hereby incorporated by reference in theirentirety. In some embodiments, the proximity sensor turns off anddisables touch screen 212 when the multifunction device is placed nearthe user's ear (e.g., when the user is making a phone call).

Device 200 optionally also includes one or more tactile outputgenerators 267. FIG. 2A shows a tactile output generator coupled tohaptic feedback controller 261 in I/O subsystem 206. Tactile outputgenerator 267 optionally includes one or more electroacoustic devicessuch as speakers or other audio components and/or electromechanicaldevices that convert energy into linear motion such as a motor,solenoid, electroactive polymer, piezoelectric actuator, electrostaticactuator, or other tactile output generating component (e.g., acomponent that converts electrical signals into tactile outputs on thedevice). Contact intensity sensor 265 receives tactile feedbackgeneration instructions from haptic feedback module 233 and generatestactile outputs on device 200 that are capable of being sensed by a userof device 200. In some embodiments, at least one tactile outputgenerator is collocated with, or proximate to, a touch-sensitive surface(e.g., touch-sensitive display system 212) and, optionally, generates atactile output by moving the touch-sensitive surface vertically (e.g.,in/out of a surface of device 200) or laterally (e.g., back and forth inthe same plane as a surface of device 200). In some embodiments, atleast one tactile output generator sensor is located on the back ofdevice 200, opposite touch screen display 212, which is located on thefront of device 200.

Device 200 also includes one or more accelerometers 268. FIG. 2A showsaccelerometer 268 coupled to peripherals interface 218. Alternately,accelerometer 268 is coupled to an input controller 260 in I/O subsystem206. Accelerometer 268 performs, for example, as described in U.S.Patent Publication No. 20050190059, “Acceleration-based Theft DetectionSystem for Portable Electronic Devices,” and U.S. Patent Publication No.20060017692, “Methods And Apparatuses For Operating A Portable DeviceBased On An Accelerometer,” both of which are incorporated by referenceherein in their entirety. In some embodiments, information is displayedon the touch screen display in a portrait view or a landscape view basedon an analysis of data received from the one or more accelerometers.Device 200 optionally includes, in addition to accelerometer(s) 268, amagnetometer (not shown) and a GPS (or GLONASS or other globalnavigation system) receiver (not shown) for obtaining informationconcerning the location and orientation (e.g., portrait or landscape) ofdevice 200.

In some embodiments, the software components stored in memory 202include operating system 226, communication module (or set ofinstructions) 228, contact/motion module (or set of instructions) 230,graphics module (or set of instructions) 232, text input module (or setof instructions) 234, Global Positioning System (GPS) module (or set ofinstructions) 235, Digital Assistant Client Module 229, and applications(or sets of instructions) 236. Further, memory 202 stores data andmodels, such as user data and models 231. Furthermore, in someembodiments, memory 202 (FIG. 2A) or 470 (FIG. 4) stores device/globalinternal state 257, as shown in FIGS. 2A and 4. Device/global internalstate 257 includes one or more of: active application state, indicatingwhich applications, if any, are currently active; display state,indicating what applications, views or other information occupy variousregions of touch screen display 212; sensor state, including informationobtained from the device's various sensors and input control devices216; and location information concerning the device's location and/orattitude.

Operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communication between varioushardware and software components.

Communication module 228 facilitates communication with other devicesover one or more external ports 224 and also includes various softwarecomponents for handling data received by RF circuitry 208 and/orexternal port 224. External port 224 (e.g., Universal Serial Bus (USB),FIREWIRE, etc.) is adapted for coupling directly to other devices orindirectly over a network (e.g., the Internet, wireless LAN, etc.). Insome embodiments, the external port is a multi-pin (e.g., 30-pin)connector that is the same as, or similar to and/or compatible with, the30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module 230 optionally detects contact with touch screen212 (in conjunction with display controller 256) and othertouch-sensitive devices (e.g., a touchpad or physical click wheel).Contact/motion module 230 includes various software components forperforming various operations related to detection of contact, such asdetermining if contact has occurred (e.g., detecting a finger-downevent), determining an intensity of the contact (e.g., the force orpressure of the contact or a substitute for the force or pressure of thecontact), determining if there is movement of the contact and trackingthe movement across the touch-sensitive surface (e.g., detecting one ormore finger-dragging events), and determining if the contact has ceased(e.g., detecting a finger-up event or a break in contact).Contact/motion module 230 receives contact data from the touch-sensitivesurface. Determining movement of the point of contact, which isrepresented by a series of contact data, optionally includes determiningspeed (magnitude), velocity (magnitude and direction), and/or anacceleration (a change in magnitude and/or direction) of the point ofcontact. These operations are, optionally, applied to single contacts(e.g., one finger contacts) or to multiple simultaneous contacts (e.g.,“multitouch”/multiple finger contacts). In some embodiments,contact/motion module 230 and display controller 256 detect contact on atouchpad.

In some embodiments, contact/motion module 230 uses a set of one or moreintensity thresholds to determine whether an operation has beenperformed by a user (e.g., to determine whether a user has “clicked” onan icon). In some embodiments, at least a subset of the intensitythresholds are determined in accordance with software parameters (e.g.,the intensity thresholds are not determined by the activation thresholdsof particular physical actuators and can be adjusted without changingthe physical hardware of device 200). For example, a mouse “click”threshold of a trackpad or touch screen display can be set to any of alarge range of predefined threshold values without changing the trackpador touch screen display hardware. Additionally, in some implementations,a user of the device is provided with software settings for adjustingone or more of the set of intensity thresholds (e.g., by adjustingindividual intensity thresholds and/or by adjusting a plurality ofintensity thresholds at once with a system-level click “intensity”parameter).

Contact/motion module 230 optionally detects a gesture input by a user.Different gestures on the touch-sensitive surface have different contactpatterns (e.g., different motions, timings, and/or intensities ofdetected contacts). Thus, a gesture is, optionally, detected bydetecting a particular contact pattern. For example, detecting a fingertap gesture includes detecting a finger-down event followed by detectinga finger-up (liftoff) event at the same position (or substantially thesame position) as the finger-down event (e.g., at the position of anicon). As another example, detecting a finger swipe gesture on thetouch-sensitive surface includes detecting a finger-down event followedby detecting one or more finger-dragging events, and subsequentlyfollowed by detecting a finger-up (liftoff) event.

Graphics module 232 includes various known software components forrendering and displaying graphics on touch screen 212 or other display,including components for changing the visual impact (e.g., brightness,transparency, saturation, contrast, or other visual property) ofgraphics that are displayed. As used herein, the term “graphics”includes any object that can be displayed to a user, including, withoutlimitation, text, web pages, icons (such as user-interface objectsincluding soft keys), digital images, videos, animations, and the like.

In some embodiments, graphics module 232 stores data representinggraphics to be used. Each graphic is, optionally, assigned acorresponding code. Graphics module 232 receives, from applicationsetc., one or more codes specifying graphics to be displayed along with,if necessary, coordinate data and other graphic property data, and thengenerates screen image data to output to display controller 256.

Haptic feedback module 233 includes various software components forgenerating instructions used by tactile output generator(s) 267 toproduce tactile outputs at one or more locations on device 200 inresponse to user interactions with device 200.

Text input module 234, which is, in some examples, a component ofgraphics module 232, provides soft keyboards for entering text invarious applications (e.g., contacts 237, email 240, IM 241, browser247, and any other application that needs text input).

GPS module 235 determines the location of the device and provides thisinformation for use in various applications (e.g., to telephone 238 foruse in location-based dialing; to camera 243 as picture/video metadata;and to applications that provide location-based services such as weatherwidgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module 229 includes various client-side digitalassistant instructions to provide the client-side functionalities of thedigital assistant. For example, digital assistant client module 229 iscapable of accepting voice input (e.g., speech input), text input, touchinput, and/or gestural input through various user interfaces (e.g.,microphone 213, accelerometer(s) 268, touch-sensitive display system212, optical sensor(s) 264, other input control devices 216, etc.) ofportable multifunction device 200. Digital assistant client module 229is also capable of providing output in audio (e.g., speech output),visual, and/or tactile forms through various output interfaces (e.g.,speaker 211, touch-sensitive display system 212, tactile outputgenerator(s) 267, etc.) of portable multifunction device 200. Forexample, output is provided as voice, sound, alerts, text messages,menus, graphics, videos, animations, vibrations, and/or combinations oftwo or more of the above. During operation, digital assistant clientmodule 229 communicates with DA server 106 using RF circuitry 208.

User data and models 231 include various data associated with the user(e.g., user-specific vocabulary data, user preference data,user-specified name pronunciations, data from the user's electronicaddress book, to-do lists, shopping lists, etc.) to provide theclient-side functionalities of the digital assistant. Further, user dataand models 231 include various models (e.g., speech recognition models,statistical language models, natural language processing models,ontology, task flow models, service models, etc.) for processing userinput and determining user intent.

In some examples, digital assistant client module 229 utilizes thevarious sensors, subsystems, and peripheral devices of portablemultifunction device 200 to gather additional information from thesurrounding environment of the portable multifunction device 200 toestablish a context associated with a user, the current userinteraction, and/or the current user input. In some examples, digitalassistant client module 229 provides the contextual information or asubset thereof with the user input to DA server 106 to help infer theuser's intent. In some examples, the digital assistant also uses thecontextual information to determine how to prepare and deliver outputsto the user. Contextual information is referred to as context data.

In some examples, the contextual information that accompanies the userinput includes sensor information, e.g., lighting, ambient noise,ambient temperature, images or videos of the surrounding environment,etc. In some examples, the contextual information can also include thephysical state of the device, e.g., device orientation, device location,device temperature, power level, speed, acceleration, motion patterns,cellular signals strength, etc. In some examples, information related tothe software state of DA server 106, e.g., running processes, installedprograms, past and present network activities, background services,error logs, resources usage, etc., and of portable multifunction device200 is provided to DA server 106 as contextual information associatedwith a user input.

In some examples, the digital assistant client module 229 selectivelyprovides information (e.g., user data 231) stored on the portablemultifunction device 200 in response to requests from DA server 106. Insome examples, digital assistant client module 229 also elicitsadditional input from the user via a natural language dialogue or otheruser interfaces upon request by DA server 106. Digital assistant clientmodule 229 passes the additional input to DA server 106 to help DAserver 106 in intent deduction and/or fulfillment of the user's intentexpressed in the user request.

A more detailed description of a digital assistant is described belowwith reference to FIGS. 7A-7C. It should be recognized that digitalassistant client module 229 can include any number of the sub-modules ofdigital assistant module 726 described below.

Applications 236 include the following modules (or sets ofinstructions), or a subset or superset thereof:

-   -   Contacts module 237 (sometimes called an address book or contact        list);    -   Telephone module 238;    -   Video conference module 239;    -   E-mail client module 240;    -   Instant messaging (IM) module 241;    -   Workout support module 242;    -   Camera module 243 for still and/or video images;    -   Image management module 244;    -   Video player module;    -   Music player module;    -   Browser module 247;    -   Calendar module 248;    -   Widget modules 249, which includes, in some examples, one or        more of: weather widget 249-1, stocks widget 249-2, calculator        widget 249-3, alarm clock widget 249-4, dictionary widget 249-5,        and other widgets obtained by the user, as well as user-created        widgets 249-6;    -   Widget creator module 250 for making user-created widgets 249-6;    -   Search module 251;    -   Video and music player module 252, which merges video player        module and music player module;    -   Notes module 253;    -   Map module 254; and/or    -   Online video module 255.

Examples of other applications 236 that are stored in memory 202 includeother word processing applications, other image editing applications,drawing applications, presentation applications, JAVA-enabledapplications, encryption, digital rights management, voice recognition,and voice replication.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, contacts module 237 are used to manage an address book or contactlist (e.g., stored in application internal state 292 of contacts module237 in memory 202 or memory 470), including: adding name(s) to theaddress book; deleting name(s) from the address book; associatingtelephone number(s), e-mail address(es), physical address(es) or otherinformation with a name; associating an image with a name; categorizingand sorting names; providing telephone numbers or e-mail addresses toinitiate and/or facilitate communications by telephone 238, videoconference module 239, e-mail 240, or IM 241; and so forth.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, contact/motionmodule 230, graphics module 232, and text input module 234, telephonemodule 238 are used to enter a sequence of characters corresponding to atelephone number, access one or more telephone numbers in contactsmodule 237, modify a telephone number that has been entered, dial arespective telephone number, conduct a conversation, and disconnect orhang up when the conversation is completed. As noted above, the wirelesscommunication uses any of a plurality of communications standards,protocols, and technologies.

In conjunction with RF circuitry 208, audio circuitry 210, speaker 211,microphone 213, touch screen 212, display controller 256, optical sensor264, optical sensor controller 258, contact/motion module 230, graphicsmodule 232, text input module 234, contacts module 237, and telephonemodule 238, video conference module 239 includes executable instructionsto initiate, conduct, and terminate a video conference between a userand one or more other participants in accordance with user instructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, e-mail client module 240 includes executableinstructions to create, send, receive, and manage e-mail in response touser instructions. In conjunction with image management module 244,e-mail client module 240 makes it very easy to create and send e-mailswith still or video images taken with camera module 243.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, the instant messaging module 241 includes executableinstructions to enter a sequence of characters corresponding to aninstant message, to modify previously entered characters, to transmit arespective instant message (for example, using a Short Message Service(SMS) or Multimedia Message Service (MMS) protocol for telephony-basedinstant messages or using XMPP, SIMPLE, or IMPS for Internet-basedinstant messages), to receive instant messages, and to view receivedinstant messages. In some embodiments, transmitted and/or receivedinstant messages include graphics, photos, audio files, video filesand/or other attachments as are supported in an MMS and/or an EnhancedMessaging Service (EMS). As used herein, “instant messaging” refers toboth telephony-based messages (e.g., messages sent using SMS or MMS) andInternet-based messages (e.g., messages sent using XMPP, SIMPLE, orIMPS).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, map module 254, and music playermodule, workout support module 242 includes executable instructions tocreate workouts (e.g., with time, distance, and/or calorie burninggoals); communicate with workout sensors (sports devices); receiveworkout sensor data; calibrate sensors used to monitor a workout; selectand play music for a workout; and display, store, and transmit workoutdata.

In conjunction with touch screen 212, display controller 256, opticalsensor(s) 264, optical sensor controller 258, contact/motion module 230,graphics module 232, and image management module 244, camera module 243includes executable instructions to capture still images or video(including a video stream) and store them into memory 202, modifycharacteristics of a still image or video, or delete a still image orvideo from memory 202.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, text input module 234,and camera module 243, image management module 244 includes executableinstructions to arrange, modify (e.g., edit), or otherwise manipulate,label, delete, present (e.g., in a digital slide show or album), andstore still and/or video images.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, and textinput module 234, browser module 247 includes executable instructions tobrowse the Internet in accordance with user instructions, includingsearching, linking to, receiving, and displaying web pages or portionsthereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, e-mail client module 240, and browser module 247,calendar module 248 includes executable instructions to create, display,modify, and store calendars and data associated with calendars (e.g.,calendar entries, to-do lists, etc.) in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, widget modules 249 aremini-applications that can be downloaded and used by a user (e.g.,weather widget 249-1, stocks widget 249-2, calculator widget 249-3,alarm clock widget 249-4, and dictionary widget 249-5) or created by theuser (e.g., user-created widget 249-6). In some embodiments, a widgetincludes an HTML (Hypertext Markup Language) file, a CSS (CascadingStyle Sheets) file, and a JavaScript file. In some embodiments, a widgetincludes an XML (Extensible Markup Language) file and a JavaScript file(e.g., Yahoo! Widgets).

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, and browser module 247, the widget creator module 250are used by a user to create widgets (e.g., turning a user-specifiedportion of a web page into a widget).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, search module 251 includes executable instructions to search fortext, music, sound, image, video, and/or other files in memory 202 thatmatch one or more search criteria (e.g., one or more user-specifiedsearch terms) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, and browser module 247, video and musicplayer module 252 includes executable instructions that allow the userto download and play back recorded music and other sound files stored inone or more file formats, such as MP3 or AAC files, and executableinstructions to display, present, or otherwise play back videos (e.g.,on touch screen 212 or on an external, connected display via externalport 224). In some embodiments, device 200 optionally includes thefunctionality of an MP3 player, such as an iPod (trademark of AppleInc.).

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, and text input module234, notes module 253 includes executable instructions to create andmanage notes, to-do lists, and the like in accordance with userinstructions.

In conjunction with RF circuitry 208, touch screen 212, displaycontroller 256, contact/motion module 230, graphics module 232, textinput module 234, GPS module 235, and browser module 247, map module 254are used to receive, display, modify, and store maps and data associatedwith maps (e.g., driving directions, data on stores and other points ofinterest at or near a particular location, and other location-baseddata) in accordance with user instructions.

In conjunction with touch screen 212, display controller 256,contact/motion module 230, graphics module 232, audio circuitry 210,speaker 211, RF circuitry 208, text input module 234, e-mail clientmodule 240, and browser module 247, online video module 255 includesinstructions that allow the user to access, browse, receive (e.g., bystreaming and/or download), play back (e.g., on the touch screen or onan external, connected display via external port 224), send an e-mailwith a link to a particular online video, and otherwise manage onlinevideos in one or more file formats, such as H.264. In some embodiments,instant messaging module 241, rather than e-mail client module 240, isused to send a link to a particular online video. Additional descriptionof the online video application can be found in U.S. Provisional PatentApplication No. 60/936,562, “Portable Multifunction Device, Method, andGraphical User Interface for Playing Online Videos,” filed Jun. 20,2007, and U.S. patent application Ser. No. 11/968,067, “PortableMultifunction Device, Method, and Graphical User Interface for PlayingOnline Videos,” filed Dec. 31, 2007, the contents of which are herebyincorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to aset of executable instructions for performing one or more functionsdescribed above and the methods described in this application (e.g., thecomputer-implemented methods and other information processing methodsdescribed herein). These modules (e.g., sets of instructions) need notbe implemented as separate software programs, procedures, or modules,and thus various subsets of these modules can be combined or otherwiserearranged in various embodiments. For example, video player module canbe combined with music player module into a single module (e.g., videoand music player module 252, FIG. 2A). In some embodiments, memory 202stores a subset of the modules and data structures identified above.Furthermore, memory 202 stores additional modules and data structuresnot described above.

In some embodiments, device 200 is a device where operation of apredefined set of functions on the device is performed exclusivelythrough a touch screen and/or a touchpad. By using a touch screen and/ora touchpad as the primary input control device for operation of device200, the number of physical input control devices (such as push buttons,dials, and the like) on device 200 is reduced.

The predefined set of functions that are performed exclusively through atouch screen and/or a touchpad optionally include navigation betweenuser interfaces. In some embodiments, the touchpad, when touched by theuser, navigates device 200 to a main, home, or root menu from any userinterface that is displayed on device 200. In such embodiments, a “menubutton” is implemented using a touchpad. In some other embodiments, themenu button is a physical push button or other physical input controldevice instead of a touchpad.

FIG. 2B is a block diagram illustrating exemplary components for eventhandling in accordance with some embodiments. In some embodiments,memory 202 (FIG. 2A) or 470 (FIG. 4) includes event sorter 270 (e.g., inoperating system 226) and a respective application 236-1 (e.g., any ofthe aforementioned applications 237-251, 255, 480-490).

Event sorter 270 receives event information and determines theapplication 236-1 and application view 291 of application 236-1 to whichto deliver the event information. Event sorter 270 includes eventmonitor 271 and event dispatcher module 274. In some embodiments,application 236-1 includes application internal state 292, whichindicates the current application view(s) displayed on touch-sensitivedisplay 212 when the application is active or executing. In someembodiments, device/global internal state 257 is used by event sorter270 to determine which application(s) is (are) currently active, andapplication internal state 292 is used by event sorter 270 to determineapplication views 291 to which to deliver event information.

In some embodiments, application internal state 292 includes additionalinformation, such as one or more of: resume information to be used whenapplication 236-1 resumes execution, user interface state informationthat indicates information being displayed or that is ready for displayby application 236-1, a state queue for enabling the user to go back toa prior state or view of application 236-1, and a redo/undo queue ofprevious actions taken by the user.

Event monitor 271 receives event information from peripherals interface218. Event information includes information about a sub-event (e.g., auser touch on touch-sensitive display 212, as part of a multi-touchgesture). Peripherals interface 218 transmits information it receivesfrom I/O subsystem 206 or a sensor, such as proximity sensor 266,accelerometer(s) 268, and/or microphone 213 (through audio circuitry210). Information that peripherals interface 218 receives from I/Osubsystem 206 includes information from touch-sensitive display 212 or atouch-sensitive surface.

In some embodiments, event monitor 271 sends requests to the peripheralsinterface 218 at predetermined intervals. In response, peripheralsinterface 218 transmits event information. In other embodiments,peripherals interface 218 transmits event information only when there isa significant event (e.g., receiving an input above a predeterminednoise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter 270 also includes a hit viewdetermination module 272 and/or an active event recognizer determinationmodule 273.

Hit view determination module 272 provides software procedures fordetermining where a sub-event has taken place within one or more viewswhen touch-sensitive display 212 displays more than one view. Views aremade up of controls and other elements that a user can see on thedisplay.

Another aspect of the user interface associated with an application is aset of views, sometimes herein called application views or userinterface windows, in which information is displayed and touch-basedgestures occur. The application views (of a respective application) inwhich a touch is detected correspond to programmatic levels within aprogrammatic or view hierarchy of the application. For example, thelowest level view in which a touch is detected is called the hit view,and the set of events that are recognized as proper inputs is determinedbased, at least in part, on the hit view of the initial touch thatbegins a touch-based gesture.

Hit view determination module 272 receives information related to subevents of a touch-based gesture. When an application has multiple viewsorganized in a hierarchy, hit view determination module 272 identifies ahit view as the lowest view in the hierarchy which should handle thesub-event. In most circumstances, the hit view is the lowest level viewin which an initiating sub-event occurs (e.g., the first sub-event inthe sequence of sub-events that form an event or potential event). Oncethe hit view is identified by the hit view determination module 272, thehit view typically receives all sub-events related to the same touch orinput source for which it was identified as the hit view.

Active event recognizer determination module 273 determines which viewor views within a view hierarchy should receive a particular sequence ofsub-events. In some embodiments, active event recognizer determinationmodule 273 determines that only the hit view should receive a particularsequence of sub-events. In other embodiments, active event recognizerdetermination module 273 determines that all views that include thephysical location of a sub-event are actively involved views, andtherefore determines that all actively involved views should receive aparticular sequence of sub-events. In other embodiments, even if touchsub-events were entirely confined to the area associated with oneparticular view, views higher in the hierarchy would still remain asactively involved views.

Event dispatcher module 274 dispatches the event information to an eventrecognizer (e.g., event recognizer 280). In embodiments including activeevent recognizer determination module 273, event dispatcher module 274delivers the event information to an event recognizer determined byactive event recognizer determination module 273. In some embodiments,event dispatcher module 274 stores in an event queue the eventinformation, which is retrieved by a respective event receiver 282.

In some embodiments, operating system 226 includes event sorter 270.Alternatively, application 236-1 includes event sorter 270. In yet otherembodiments, event sorter 270 is a stand-alone module, or a part ofanother module stored in memory 202, such as contact/motion module 230.

In some embodiments, application 236-1 includes a plurality of eventhandlers 290 and one or more application views 291, each of whichincludes instructions for handling touch events that occur within arespective view of the application's user interface. Each applicationview 291 of the application 236-1 includes one or more event recognizers280. Typically, a respective application view 291 includes a pluralityof event recognizers 280. In other embodiments, one or more of eventrecognizers 280 are part of a separate module, such as a user interfacekit (not shown) or a higher level object from which application 236-1inherits methods and other properties. In some embodiments, a respectiveevent handler 290 includes one or more of: data updater 276, objectupdater 277, GUI updater 278, and/or event data 279 received from eventsorter 270. Event handler 290 utilizes or calls data updater 276, objectupdater 277, or GUI updater 278 to update the application internal state292. Alternatively, one or more of the application views 291 include oneor more respective event handlers 290. Also, in some embodiments, one ormore of data updater 276, object updater 277, and GUI updater 278 areincluded in a respective application view 291.

A respective event recognizer 280 receives event information (e.g.,event data 279) from event sorter 270 and identifies an event from theevent information. Event recognizer 280 includes event receiver 282 andevent comparator 284. In some embodiments, event recognizer 280 alsoincludes at least a subset of: metadata 283, and event deliveryinstructions 288 (which include sub-event delivery instructions).

Event receiver 282 receives event information from event sorter 270. Theevent information includes information about a sub-event, for example, atouch or a touch movement. Depending on the sub-event, the eventinformation also includes additional information, such as location ofthe sub-event. When the sub-event concerns motion of a touch, the eventinformation also includes speed and direction of the sub-event. In someembodiments, events include rotation of the device from one orientationto another (e.g., from a portrait orientation to a landscapeorientation, or vice versa), and the event information includescorresponding information about the current orientation (also calleddevice attitude) of the device.

Event comparator 284 compares the event information to predefined eventor sub-event definitions and, based on the comparison, determines anevent or sub event, or determines or updates the state of an event orsub-event. In some embodiments, event comparator 284 includes eventdefinitions 286. Event definitions 286 contain definitions of events(e.g., predefined sequences of sub-events), for example, event 1(287-1), event 2 (287-2), and others. In some embodiments, sub-events inan event (287) include, for example, touch begin, touch end, touchmovement, touch cancellation, and multiple touching. In one example, thedefinition for event 1 (287-1) is a double tap on a displayed object.The double tap, for example, comprises a first touch (touch begin) onthe displayed object for a predetermined phase, a first liftoff (touchend) for a predetermined phase, a second touch (touch begin) on thedisplayed object for a predetermined phase, and a second liftoff (touchend) for a predetermined phase. In another example, the definition forevent 2 (287-2) is a dragging on a displayed object. The dragging, forexample, comprises a touch (or contact) on the displayed object for apredetermined phase, a movement of the touch across touch-sensitivedisplay 212, and liftoff of the touch (touch end). In some embodiments,the event also includes information for one or more associated eventhandlers 290.

In some embodiments, event definition 287 includes a definition of anevent for a respective user-interface object. In some embodiments, eventcomparator 284 performs a hit test to determine which user-interfaceobject is associated with a sub-event. For example, in an applicationview in which three user-interface objects are displayed ontouch-sensitive display 212, when a touch is detected on touch-sensitivedisplay 212, event comparator 284 performs a hit test to determine whichof the three user-interface objects is associated with the touch(sub-event). If each displayed object is associated with a respectiveevent handler 290, the event comparator uses the result of the hit testto determine which event handler 290 should be activated. For example,event comparator 284 selects an event handler associated with thesub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) alsoincludes delayed actions that delay delivery of the event informationuntil after it has been determined whether the sequence of sub-eventsdoes or does not correspond to the event recognizer's event type.

When a respective event recognizer 280 determines that the series ofsub-events do not match any of the events in event definitions 286, therespective event recognizer 280 enters an event impossible, eventfailed, or event ended state, after which it disregards subsequentsub-events of the touch-based gesture. In this situation, other eventrecognizers, if any, that remain active for the hit view continue totrack and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer 280 includes metadata283 with configurable properties, flags, and/or lists that indicate howthe event delivery system should perform sub-event delivery to activelyinvolved event recognizers. In some embodiments, metadata 283 includesconfigurable properties, flags, and/or lists that indicate how eventrecognizers interact, or are enabled to interact, with one another. Insome embodiments, metadata 283 includes configurable properties, flags,and/or lists that indicate whether sub-events are delivered to varyinglevels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer 280 activates eventhandler 290 associated with an event when one or more particularsub-events of an event are recognized. In some embodiments, a respectiveevent recognizer 280 delivers event information associated with theevent to event handler 290. Activating an event handler 290 is distinctfrom sending (and deferred sending) sub-events to a respective hit view.In some embodiments, event recognizer 280 throws a flag associated withthe recognized event, and event handler 290 associated with the flagcatches the flag and performs a predefined process.

In some embodiments, event delivery instructions 288 include sub-eventdelivery instructions that deliver event information about a sub-eventwithout activating an event handler. Instead, the sub-event deliveryinstructions deliver event information to event handlers associated withthe series of sub-events or to actively involved views. Event handlersassociated with the series of sub-events or with actively involved viewsreceive the event information and perform a predetermined process.

In some embodiments, data updater 276 creates and updates data used inapplication 236-1. For example, data updater 276 updates the telephonenumber used in contacts module 237, or stores a video file used in videoplayer module. In some embodiments, object updater 277 creates andupdates objects used in application 236-1. For example, object updater277 creates a new user-interface object or updates the position of auser-interface object. GUI updater 278 updates the GUI. For example, GUIupdater 278 prepares display information and sends it to graphics module232 for display on a touch-sensitive display.

In some embodiments, event handler(s) 290 includes or has access to dataupdater 276, object updater 277, and GUI updater 278. In someembodiments, data updater 276, object updater 277, and GUI updater 278are included in a single module of a respective application 236-1 orapplication view 291. In other embodiments, they are included in two ormore software modules.

It shall be understood that the foregoing discussion regarding eventhandling of user touches on touch-sensitive displays also applies toother forms of user inputs to operate multifunction devices 200 withinput devices, not all of which are initiated on touch screens. Forexample, mouse movement and mouse button presses, optionally coordinatedwith single or multiple keyboard presses or holds; contact movementssuch as taps, drags, scrolls, etc. on touchpads; pen stylus inputs;movement of the device; oral instructions; detected eye movements;biometric inputs; and/or any combination thereof are optionally utilizedas inputs corresponding to sub-events which define an event to berecognized.

FIG. 3 illustrates a portable multifunction device 200 having a touchscreen 212 in accordance with some embodiments. The touch screenoptionally displays one or more graphics within user interface (UI) 300.In this embodiment, as well as others described below, a user is enabledto select one or more of the graphics by making a gesture on thegraphics, for example, with one or more fingers 302 (not drawn to scalein the figure) or one or more styluses 303 (not drawn to scale in thefigure). In some embodiments, selection of one or more graphics occurswhen the user breaks contact with the one or more graphics. In someembodiments, the gesture optionally includes one or more taps, one ormore swipes (from left to right, right to left, upward and/or downward),and/or a rolling of a finger (from right to left, left to right, upwardand/or downward) that has made contact with device 200. In someimplementations or circumstances, inadvertent contact with a graphicdoes not select the graphic. For example, a swipe gesture that sweepsover an application icon optionally does not select the correspondingapplication when the gesture corresponding to selection is a tap.

Device 200 also includes one or more physical buttons, such as “home” ormenu button 304. As described previously, menu button 304 is used tonavigate to any application 236 in a set of applications that isexecuted on device 200. Alternatively, in some embodiments, the menubutton is implemented as a soft key in a GUI displayed on touch screen212.

In one embodiment, device 200 includes touch screen 212, menu button304, push button 306 for powering the device on/off and locking thedevice, volume adjustment button(s) 308, subscriber identity module(SIM) card slot 310, headset jack 312, and docking/charging externalport 224. Push button 306 is, optionally, used to turn the power on/offon the device by depressing the button and holding the button in thedepressed state for a predefined time interval; to lock the device bydepressing the button and releasing the button before the predefinedtime interval has elapsed; and/or to unlock the device or initiate anunlock process. In an alternative embodiment, device 200 also acceptsverbal input for activation or deactivation of some functions throughmicrophone 213. Device 200 also, optionally, includes one or morecontact intensity sensors 265 for detecting intensity of contacts ontouch screen 212 and/or one or more tactile output generators 267 forgenerating tactile outputs for a user of device 200.

FIG. 4 is a block diagram of an exemplary multifunction device with adisplay and a touch-sensitive surface in accordance with someembodiments. Device 400 need not be portable. In some embodiments,device 400 is a laptop computer, a desktop computer, a tablet computer,a multimedia player device, a navigation device, an educational device(such as a child's learning toy), a gaming system, or a control device(e.g., a home or industrial controller). Device 400 typically includesone or more processing units (CPUs) 410, one or more network or othercommunications interfaces 460, memory 470, and one or more communicationbuses 420 for interconnecting these components. Communication buses 420optionally include circuitry (sometimes called a chipset) thatinterconnects and controls communications between system components.Device 400 includes input/output (I/O) interface 430 comprising display440, which is typically a touch screen display. I/O interface 430 alsooptionally includes a keyboard and/or mouse (or other pointing device)450 and touchpad 455, tactile output generator 457 for generatingtactile outputs on device 400 (e.g., similar to tactile outputgenerator(s) 267 described above with reference to FIG. 2A), sensors 459(e.g., optical, acceleration, proximity, touch-sensitive, and/or contactintensity sensors similar to contact intensity sensor(s) 265 describedabove with reference to FIG. 2A). Memory 470 includes high-speed randomaccess memory, such as DRAM, SRAM, DDR RAM, or other random access solidstate memory devices; and optionally includes non-volatile memory, suchas one or more magnetic disk storage devices, optical disk storagedevices, flash memory devices, or other non-volatile solid state storagedevices. Memory 470 optionally includes one or more storage devicesremotely located from CPU(s) 410. In some embodiments, memory 470 storesprograms, modules, and data structures analogous to the programs,modules, and data structures stored in memory 202 of portablemultifunction device 200 (FIG. 2A), or a subset thereof. Furthermore,memory 470 optionally stores additional programs, modules, and datastructures not present in memory 202 of portable multifunction device200. For example, memory 470 of device 400 optionally stores drawingmodule 480, presentation module 482, word processing module 484, websitecreation module 486, disk authoring module 488, and/or spreadsheetmodule 490, while memory 202 of portable multifunction device 200 (FIG.2A) optionally does not store these modules.

Each of the above-identified elements in FIG. 4 is, in some examples,stored in one or more of the previously mentioned memory devices. Eachof the above-identified modules corresponds to a set of instructions forperforming a function described above. The above-identified modules orprograms (e.g., sets of instructions) need not be implemented asseparate software programs, procedures, or modules, and thus varioussubsets of these modules are combined or otherwise rearranged in variousembodiments. In some embodiments, memory 470 stores a subset of themodules and data structures identified above. Furthermore, memory 470stores additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces thatcan be implemented on, for example, portable multifunction device 200.

FIG. 5A illustrates an exemplary user interface for a menu ofapplications on portable multifunction device 200 in accordance withsome embodiments. Similar user interfaces are implemented on device 400.In some embodiments, user interface 500 includes the following elements,or a subset or superset thereof:

Signal strength indicator(s) 502 for wireless communication(s), such ascellular and Wi-Fi signals;

-   -   Time 504;    -   Bluetooth indicator 505;    -   Battery status indicator 506;    -   Tray 508 with icons for frequently used applications, such as:        -   Icon 516 for telephone module 238, labeled “Phone,” which            optionally includes an indicator 514 of the number of missed            calls or voicemail messages;        -   Icon 518 for e-mail client module 240, labeled “Mail,” which            optionally includes an indicator 510 of the number of unread            e-mails;        -   Icon 520 for browser module 247, labeled “Browser;” and        -   Icon 522 for video and music player module 252, also            referred to as iPod (trademark of Apple Inc.) module 252,            labeled “iPod;” and    -   Icons for other applications, such as:        -   Icon 524 for IM module 241, labeled “Messages;”        -   Icon 526 for calendar module 248, labeled “Calendar;”        -   Icon 528 for image management module 244, labeled “Photos;”        -   Icon 530 for camera module 243, labeled “Camera;”        -   Icon 532 for online video module 255, labeled “Online            Video;”        -   Icon 534 for stocks widget 249-2, labeled “Stocks;”        -   Icon 536 for map module 254, labeled “Maps;”        -   Icon 538 for weather widget 249-1, labeled “Weather;”        -   Icon 540 for alarm clock widget 249-4, labeled “Clock;”        -   Icon 542 for workout support module 242, labeled “Workout            Support;”        -   Icon 544 for notes module 253, labeled “Notes;” and        -   Icon 546 for a settings application or module, labeled            “Settings,” which provides access to settings for device 200            and its various applications 236.

It should be noted that the icon labels illustrated in FIG. 5A aremerely exemplary. For example, icon 522 for video and music playermodule 252 is optionally labeled “Music” or “Music Player.” Other labelsare, optionally, used for various application icons. In someembodiments, a label for a respective application icon includes a nameof an application corresponding to the respective application icon. Insome embodiments, a label for a particular application icon is distinctfrom a name of an application corresponding to the particularapplication icon.

FIG. 5B illustrates an exemplary user interface on a device (e.g.,device 400, FIG. 4) with a touch-sensitive surface 551 (e.g., a tabletor touchpad 455, FIG. 4) that is separate from the display 550 (e.g.,touch screen display 212). Device 400 also, optionally, includes one ormore contact intensity sensors (e.g., one or more of sensors 457) fordetecting intensity of contacts on touch-sensitive surface 551 and/orone or more tactile output generators 459 for generating tactile outputsfor a user of device 400.

Although some of the examples which follow will be given with referenceto inputs on touch screen display 212 (where the touch-sensitive surfaceand the display are combined), in some embodiments, the device detectsinputs on a touch-sensitive surface that is separate from the display,as shown in FIG. 5B. In some embodiments, the touch-sensitive surface(e.g., 551 in FIG. 5B) has a primary axis (e.g., 552 in FIG. 5B) thatcorresponds to a primary axis (e.g., 553 in FIG. 5B) on the display(e.g., 550). In accordance with these embodiments, the device detectscontacts (e.g., 560 and 562 in FIG. 5B) with the touch-sensitive surface551 at locations that correspond to respective locations on the display(e.g., in FIG. 5B, 560 corresponds to 568 and 562 corresponds to 570).In this way, user inputs (e.g., contacts 560 and 562, and movementsthereof) detected by the device on the touch-sensitive surface (e.g.,551 in FIG. 5B) are used by the device to manipulate the user interfaceon the display (e.g., 550 in FIG. 5B) of the multifunction device whenthe touch-sensitive surface is separate from the display. It should beunderstood that similar methods are, optionally, used for other userinterfaces described herein.

Additionally, while the following examples are given primarily withreference to finger inputs (e.g., finger contacts, finger tap gestures,finger swipe gestures), it should be understood that, in someembodiments, one or more of the finger inputs are replaced with inputfrom another input device (e.g., a mouse-based input or stylus input).For example, a swipe gesture is, optionally, replaced with a mouse click(e.g., instead of a contact) followed by movement of the cursor alongthe path of the swipe (e.g., instead of movement of the contact). Asanother example, a tap gesture is, optionally, replaced with a mouseclick while the cursor is located over the location of the tap gesture(e.g., instead of detection of the contact followed by ceasing to detectthe contact). Similarly, when multiple user inputs are simultaneouslydetected, it should be understood that multiple computer mice are,optionally, used simultaneously, or a mouse and finger contacts are,optionally, used simultaneously.

FIG. 6A illustrates exemplary personal electronic device 600. Device 600includes body 602. In some embodiments, device 600 includes some or allof the features described with respect to devices 200 and 400 (e.g.,FIGS. 2A-4). In some embodiments, device 600 has touch-sensitive displayscreen 604, hereafter touch screen 604. Alternatively, or in addition totouch screen 604, device 600 has a display and a touch-sensitivesurface. As with devices 200 and 400, in some embodiments, touch screen604 (or the touch-sensitive surface) has one or more intensity sensorsfor detecting intensity of contacts (e.g., touches) being applied. Theone or more intensity sensors of touch screen 604 (or thetouch-sensitive surface) provide output data that represents theintensity of touches. The user interface of device 600 responds totouches based on their intensity, meaning that touches of differentintensities can invoke different user interface operations on device600.

Techniques for detecting and processing touch intensity are found, forexample, in related applications: International Patent ApplicationSerial No. PCT/US2013/040061, titled “Device, Method, and Graphical UserInterface for Displaying User Interface Objects Corresponding to anApplication,” filed May 8, 2013, and International Patent ApplicationSerial No. PCT/US2013/069483, titled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” filed Nov. 11, 2013, each of which is herebyincorporated by reference in their entirety.

In some embodiments, device 600 has one or more input mechanisms 606 and608. Input mechanisms 606 and 608, if included, are physical. Examplesof physical input mechanisms include push buttons and rotatablemechanisms. In some embodiments, device 600 has one or more attachmentmechanisms. Such attachment mechanisms, if included, can permitattachment of device 600 with, for example, hats, eyewear, earrings,necklaces, shirts, jackets, bracelets, watch straps, chains, trousers,belts, shoes, purses, backpacks, and so forth. These attachmentmechanisms permit device 600 to be worn by a user.

FIG. 6B depicts exemplary personal electronic device 600. In someembodiments, device 600 includes some or all of the components describedwith respect to FIGS. 2A, 2B, and 4. Device 600 has bus 612 thatoperatively couples I/O section 614 with one or more computer processors616 and memory 618. I/O section 614 is connected to display 604, whichcan have touch-sensitive component 622 and, optionally, touch-intensitysensitive component 624. In addition, I/O section 614 is connected withcommunication unit 630 for receiving application and operating systemdata, using Wi-Fi, Bluetooth, near field communication (NFC), cellular,and/or other wireless communication techniques. Device 600 includesinput mechanisms 606 and/or 608. Input mechanism 606 is a rotatableinput device or a depressible and rotatable input device, for example.Input mechanism 608 is a button, in some examples.

Input mechanism 608 is a microphone, in some examples. Personalelectronic device 600 includes, for example, various sensors, such asGPS sensor 632, accelerometer 634, directional sensor 640 (e.g.,compass), gyroscope 636, motion sensor 638, and/or a combinationthereof, all of which are operatively connected to I/O section 614.

Memory 618 of personal electronic device 600 is a non-transitorycomputer-readable storage medium, for storing computer-executableinstructions, which, when executed by one or more computer processors616, for example, cause the computer processors to perform thetechniques and processes described below. The computer-executableinstructions, for example, are also stored and/or transported within anynon-transitory computer-readable storage medium for use by or inconnection with an instruction execution system, apparatus, or device,such as a computer-based system, processor-containing system, or othersystem that can fetch the instructions from the instruction executionsystem, apparatus, or device and execute the instructions. Personalelectronic device 600 is not limited to the components and configurationof FIG. 6B, but can include other or additional components in multipleconfigurations.

As used here, the term “affordance” refers to a user-interactivegraphical user interface object that is, for example, displayed on thedisplay screen of devices 104, 200, 400, and/or 600 (FIGS. 1, 2, 4, and6). For example, an image (e.g., icon), a button, and text (e.g.,hyperlink) each constitutes an affordance.

As used herein, the term “focus selector” refers to an input elementthat indicates a current part of a user interface with which a user isinteracting. In some implementations that include a cursor or otherlocation marker, the cursor acts as a “focus selector” so that when aninput (e.g., a press input) is detected on a touch-sensitive surface(e.g., touchpad 455 in FIG. 4 or touch-sensitive surface 551 in FIG. 5B)while the cursor is over a particular user interface element (e.g., abutton, window, slider or other user interface element), the particularuser interface element is adjusted in accordance with the detectedinput. In some implementations that include a touch screen display(e.g., touch-sensitive display system 212 in FIG. 2A or touch screen 212in FIG. 5A) that enables direct interaction with user interface elementson the touch screen display, a detected contact on the touch screen actsas a “focus selector” so that when an input (e.g., a press input by thecontact) is detected on the touch screen display at a location of aparticular user interface element (e.g., a button, window, slider, orother user interface element), the particular user interface element isadjusted in accordance with the detected input. In some implementations,focus is moved from one region of a user interface to another region ofthe user interface without corresponding movement of a cursor ormovement of a contact on a touch screen display (e.g., by using a tabkey or arrow keys to move focus from one button to another button); inthese implementations, the focus selector moves in accordance withmovement of focus between different regions of the user interface.Without regard to the specific form taken by the focus selector, thefocus selector is generally the user interface element (or contact on atouch screen display) that is controlled by the user so as tocommunicate the user's intended interaction with the user interface(e.g., by indicating, to the device, the element of the user interfacewith which the user is intending to interact). For example, the locationof a focus selector (e.g., a cursor, a contact, or a selection box) overa respective button while a press input is detected on thetouch-sensitive surface (e.g., a touchpad or touch screen) will indicatethat the user is intending to activate the respective button (as opposedto other user interface elements shown on a display of the device).

As used in the specification and claims, the term “characteristicintensity” of a contact refers to a characteristic of the contact basedon one or more intensities of the contact. In some embodiments, thecharacteristic intensity is based on multiple intensity samples. Thecharacteristic intensity is, optionally, based on a predefined number ofintensity samples, or a set of intensity samples collected during apredetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10seconds) relative to a predefined event (e.g., after detecting thecontact, prior to detecting liftoff of the contact, before or afterdetecting a start of movement of the contact, prior to detecting an endof the contact, before or after detecting an increase in intensity ofthe contact, and/or before or after detecting a decrease in intensity ofthe contact). A characteristic intensity of a contact is, optionallybased on one or more of: a maximum value of the intensities of thecontact, a mean value of the intensities of the contact, an averagevalue of the intensities of the contact, a top 10 percentile value ofthe intensities of the contact, a value at the half maximum of theintensities of the contact, a value at the 90 percent maximum of theintensities of the contact, or the like. In some embodiments, theduration of the contact is used in determining the characteristicintensity (e.g., when the characteristic intensity is an average of theintensity of the contact over time). In some embodiments, thecharacteristic intensity is compared to a set of one or more intensitythresholds to determine whether an operation has been performed by auser. For example, the set of one or more intensity thresholds includesa first intensity threshold and a second intensity threshold. In thisexample, a contact with a characteristic intensity that does not exceedthe first threshold results in a first operation, a contact with acharacteristic intensity that exceeds the first intensity threshold anddoes not exceed the second intensity threshold results in a secondoperation, and a contact with a characteristic intensity that exceedsthe second threshold results in a third operation. In some embodiments,a comparison between the characteristic intensity and one or morethresholds is used to determine whether or not to perform one or moreoperations (e.g., whether to perform a respective operation or forgoperforming the respective operation) rather than being used to determinewhether to perform a first operation or a second operation.

In some embodiments, a portion of a gesture is identified for purposesof determining a characteristic intensity. For example, atouch-sensitive surface receives a continuous swipe contacttransitioning from a start location and reaching an end location, atwhich point the intensity of the contact increases. In this example, thecharacteristic intensity of the contact at the end location is based ononly a portion of the continuous swipe contact, and not the entire swipecontact (e.g., only the portion of the swipe contact at the endlocation). In some embodiments, a smoothing algorithm is applied to theintensities of the swipe contact prior to determining the characteristicintensity of the contact. For example, the smoothing algorithmoptionally includes one or more of: an unweighted sliding-averagesmoothing algorithm, a triangular smoothing algorithm, a median filtersmoothing algorithm, and/or an exponential smoothing algorithm. In somecircumstances, these smoothing algorithms eliminate narrow spikes ordips in the intensities of the swipe contact for purposes of determininga characteristic intensity.

The intensity of a contact on the touch-sensitive surface ischaracterized relative to one or more intensity thresholds, such as acontact-detection intensity threshold, a light press intensitythreshold, a deep press intensity threshold, and/or one or more otherintensity thresholds. In some embodiments, the light press intensitythreshold corresponds to an intensity at which the device will performoperations typically associated with clicking a button of a physicalmouse or a trackpad. In some embodiments, the deep press intensitythreshold corresponds to an intensity at which the device will performoperations that are different from operations typically associated withclicking a button of a physical mouse or a trackpad. In someembodiments, when a contact is detected with a characteristic intensitybelow the light press intensity threshold (e.g., and above a nominalcontact-detection intensity threshold below which the contact is nolonger detected), the device will move a focus selector in accordancewith movement of the contact on the touch-sensitive surface withoutperforming an operation associated with the light press intensitythreshold or the deep press intensity threshold. Generally, unlessotherwise stated, these intensity thresholds are consistent betweendifferent sets of user interface figures.

An increase of characteristic intensity of the contact from an intensitybelow the light press intensity threshold to an intensity between thelight press intensity threshold and the deep press intensity thresholdis sometimes referred to as a “light press” input. An increase ofcharacteristic intensity of the contact from an intensity below the deeppress intensity threshold to an intensity above the deep press intensitythreshold is sometimes referred to as a “deep press” input. An increaseof characteristic intensity of the contact from an intensity below thecontact-detection intensity threshold to an intensity between thecontact-detection intensity threshold and the light press intensitythreshold is sometimes referred to as detecting the contact on thetouch-surface. A decrease of characteristic intensity of the contactfrom an intensity above the contact-detection intensity threshold to anintensity below the contact-detection intensity threshold is sometimesreferred to as detecting liftoff of the contact from the touch-surface.In some embodiments, the contact-detection intensity threshold is zero.In some embodiments, the contact-detection intensity threshold isgreater than zero.

In some embodiments described herein, one or more operations areperformed in response to detecting a gesture that includes a respectivepress input or in response to detecting the respective press inputperformed with a respective contact (or a plurality of contacts), wherethe respective press input is detected based at least in part ondetecting an increase in intensity of the contact (or plurality ofcontacts) above a press-input intensity threshold. In some embodiments,the respective operation is performed in response to detecting theincrease in intensity of the respective contact above the press-inputintensity threshold (e.g., a “down stroke” of the respective pressinput). In some embodiments, the press input includes an increase inintensity of the respective contact above the press-input intensitythreshold and a subsequent decrease in intensity of the contact belowthe press-input intensity threshold, and the respective operation isperformed in response to detecting the subsequent decrease in intensityof the respective contact below the press-input threshold (e.g., an “upstroke” of the respective press input).

In some embodiments, the device employs intensity hysteresis to avoidaccidental inputs sometimes termed “jitter,” where the device defines orselects a hysteresis intensity threshold with a predefined relationshipto the press-input intensity threshold (e.g., the hysteresis intensitythreshold is X intensity units lower than the press-input intensitythreshold or the hysteresis intensity threshold is 75%, 90%, or somereasonable proportion of the press-input intensity threshold). Thus, insome embodiments, the press input includes an increase in intensity ofthe respective contact above the press-input intensity threshold and asubsequent decrease in intensity of the contact below the hysteresisintensity threshold that corresponds to the press-input intensitythreshold, and the respective operation is performed in response todetecting the subsequent decrease in intensity of the respective contactbelow the hysteresis intensity threshold (e.g., an “up stroke” of therespective press input). Similarly, in some embodiments, the press inputis detected only when the device detects an increase in intensity of thecontact from an intensity at or below the hysteresis intensity thresholdto an intensity at or above the press-input intensity threshold and,optionally, a subsequent decrease in intensity of the contact to anintensity at or below the hysteresis intensity, and the respectiveoperation is performed in response to detecting the press input (e.g.,the increase in intensity of the contact or the decrease in intensity ofthe contact, depending on the circumstances).

For ease of explanation, the descriptions of operations performed inresponse to a press input associated with a press-input intensitythreshold or in response to a gesture including the press input are,optionally, triggered in response to detecting either: an increase inintensity of a contact above the press-input intensity threshold, anincrease in intensity of a contact from an intensity below thehysteresis intensity threshold to an intensity above the press-inputintensity threshold, a decrease in intensity of the contact below thepress-input intensity threshold, and/or a decrease in intensity of thecontact below the hysteresis intensity threshold corresponding to thepress-input intensity threshold. Additionally, in examples where anoperation is described as being performed in response to detecting adecrease in intensity of a contact below the press-input intensitythreshold, the operation is, optionally, performed in response todetecting a decrease in intensity of the contact below a hysteresisintensity threshold corresponding to, and lower than, the press-inputintensity threshold.

3. Digital Assistant System

FIG. 7A illustrates a block diagram of digital assistant system 700 inaccordance with various examples. In some examples, digital assistantsystem 700 is implemented on a standalone computer system. In someexamples, digital assistant system 700 is distributed across multiplecomputers. In some examples, some of the modules and functions of thedigital assistant are divided into a server portion and a clientportion, where the client portion resides on one or more user devices(e.g., devices 104, 122, 200, 400, or 600) and communicates with theserver portion (e.g., server system 108) through one or more networks,e.g., as shown in FIG. 1. In some examples, digital assistant system 700is an implementation of server system 108 (and/or DA server 106) shownin FIG. 1. It should be noted that digital assistant system 700 is onlyone example of a digital assistant system, and that digital assistantsystem 700 can have more or fewer components than shown, can combine twoor more components, or can have a different configuration or arrangementof the components. The various components shown in FIG. 7A areimplemented in hardware, software instructions for execution by one ormore processors, firmware, including one or more signal processingand/or application specific integrated circuits, or a combinationthereof.

Digital assistant system 700 includes memory 702, one or more processors704, input/output (I/O) interface 706, and network communicationsinterface 708. These components can communicate with one another overone or more communication buses or signal lines 710.

In some examples, memory 702 includes a non-transitory computer-readablemedium, such as high-speed random access memory and/or a non-volatilecomputer-readable storage medium (e.g., one or more magnetic diskstorage devices, flash memory devices, or other non-volatile solid-statememory devices).

In some examples, I/O interface 706 couples input/output devices 716 ofdigital assistant system 700, such as displays, keyboards, touchscreens, and microphones, to user interface module 722. I/O interface706, in conjunction with user interface module 722, receives user inputs(e.g., voice input, keyboard inputs, touch inputs, etc.) and processesthem accordingly. In some examples, e.g., when the digital assistant isimplemented on a standalone user device, digital assistant system 700includes any of the components and I/O communication interfacesdescribed with respect to devices 200, 400, or 600 in FIGS. 2A, 4, 6A-B,respectively. In some examples, digital assistant system 700 representsthe server portion of a digital assistant implementation, and caninteract with the user through a client-side portion residing on a userdevice (e.g., devices 104, 200, 400, or 600).

In some examples, the network communications interface 708 includeswired communication port(s) 712 and/or wireless transmission andreception circuitry 714. The wired communication port(s) receives andsend communication signals via one or more wired interfaces, e.g.,Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wirelesscircuitry 714 receives and sends RF signals and/or optical signalsfrom/to communications networks and other communications devices. Thewireless communications use any of a plurality of communicationsstandards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA,Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communicationprotocol. Network communications interface 708 enables communicationbetween digital assistant system 700 with networks, such as theInternet, an intranet, and/or a wireless network, such as a cellulartelephone network, a wireless local area network (LAN), and/or ametropolitan area network (MAN), and other devices.

In some examples, memory 702, or the computer-readable storage media ofmemory 702, stores programs, modules, instructions, and data structuresincluding all or a subset of: operating system 718, communicationsmodule 720, user interface module 722, one or more applications 724, anddigital assistant module 726. In particular, memory 702, or thecomputer-readable storage media of memory 702, stores instructions forperforming the processes described below. One or more processors 704execute these programs, modules, and instructions, and reads/writesfrom/to the data structures.

Operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X,WINDOWS, or an embedded operating system such as VxWorks) includesvarious software components and/or drivers for controlling and managinggeneral system tasks (e.g., memory management, storage device control,power management, etc.) and facilitates communications between varioushardware, firmware, and software components.

Communications module 720 facilitates communications between digitalassistant system 700 with other devices over network communicationsinterface 708. For example, communications module 720 communicates withRF circuitry 208 of electronic devices such as devices 200, 400, and 600shown in FIG. 2A, 4, 6A-B, respectively. Communications module 720 alsoincludes various components for handling data received by wirelesscircuitry 714 and/or wired communications port 712.

User interface module 722 receives commands and/or inputs from a uservia I/O interface 706 (e.g., from a keyboard, touch screen, pointingdevice, controller, and/or microphone), and generate user interfaceobjects on a display. User interface module 722 also prepares anddelivers outputs (e.g., speech, sound, animation, text, icons,vibrations, haptic feedback, light, etc.) to the user via the I/Ointerface 706 (e.g., through displays, audio channels, speakers,touch-pads, etc.).

Applications 724 include programs and/or modules that are configured tobe executed by one or more processors 704. For example, if the digitalassistant system is implemented on a standalone user device,applications 724 include user applications, such as games, a calendarapplication, a navigation application, or an email application. Ifdigital assistant system 700 is implemented on a server, applications724 include resource management applications, diagnostic applications,or scheduling applications, for example.

Memory 702 also stores digital assistant module 726 (or the serverportion of a digital assistant). In some examples, digital assistantmodule 726 includes the following sub-modules, or a subset or supersetthereof: input/output processing module 728, speech-to-text (STT)processing module 730, natural language processing module 732, dialogueflow processing module 734, task flow processing module 736, serviceprocessing module 738, and speech synthesis processing module 740. Eachof these modules has access to one or more of the following systems ordata and models of the digital assistant module 726, or a subset orsuperset thereof: ontology 760, vocabulary index 744, user data 748,task flow models 754, service models 756, and ASR systems 758.

In some examples, using the processing modules, data, and modelsimplemented in digital assistant module 726, the digital assistant canperform at least some of the following: converting speech input intotext; identifying a user's intent expressed in a natural language inputreceived from the user; actively eliciting and obtaining informationneeded to fully infer the user's intent (e.g., by disambiguating words,games, intentions, etc.); determining the task flow for fulfilling theinferred intent; and executing the task flow to fulfill the inferredintent.

In some examples, as shown in FIG. 7B, I/O processing module 728interacts with the user through I/O devices 716 in FIG. 7A or with auser device (e.g., devices 104, 200, 400, or 600) through networkcommunications interface 708 in FIG. 7A to obtain user input (e.g., aspeech input) and to provide responses (e.g., as speech outputs) to theuser input. I/O processing module 728 optionally obtains contextualinformation associated with the user input from the user device, alongwith or shortly after the receipt of the user input. The contextualinformation includes user-specific data, vocabulary, and/or preferencesrelevant to the user input. In some examples, the contextual informationalso includes software and hardware states of the user device at thetime the user request is received, and/or information related to thesurrounding environment of the user at the time that the user requestwas received. In some examples, I/O processing module 728 also sendsfollow-up questions to, and receive answers from, the user regarding theuser request. When a user request is received by I/O processing module728 and the user request includes speech input, I/O processing module728 forwards the speech input to STT processing module 730 (or speechrecognizer) for speech-to-text conversions.

STT processing module 730 includes one or more ASR systems 758. The oneor more ASR systems 758 can process the speech input that is receivedthrough I/O processing module 728 to produce a recognition result. EachASR system 758 includes a front-end speech pre-processor. The front-endspeech pre-processor extracts representative features from the speechinput. For example, the front-end speech pre-processor performs aFourier transform on the speech input to extract spectral features thatcharacterize the speech input as a sequence of representativemulti-dimensional vectors. Further, each ASR system 758 includes one ormore speech recognition models (e.g., acoustic models and/or languagemodels) and implements one or more speech recognition engines. Examplesof speech recognition models include Hidden Markov Models,Gaussian-Mixture Models, Deep Neural Network Models, n-gram languagemodels, and other statistical models. Examples of speech recognitionengines include the dynamic time warping based engines and weightedfinite-state transducers (WFST) based engines. The one or more speechrecognition models and the one or more speech recognition engines areused to process the extracted representative features of the front-endspeech pre-processor to produce intermediate recognitions results (e.g.,phonemes, phonemic strings, and sub-words), and ultimately, textrecognition results (e.g., words, word strings, or sequence of tokens).In some examples, the speech input is processed at least partially by athird-party service or on the user's device (e.g., device 104, 200, 400,or 600) to produce the recognition result. Once STT processing module730 produces recognition results containing a text string (e.g., words,or sequence of words, or sequence of tokens), the recognition result ispassed to natural language processing module 732 for intent deduction.In some examples, STT processing module 730 produces multiple candidatetext representations of the speech input. Each candidate textrepresentation is a sequence of words or tokens corresponding to thespeech input. In some examples, each candidate text representation isassociated with a speech recognition confidence score. Based on thespeech recognition confidence scores, STT processing module 730 ranksthe candidate text representations and provides the n-best (e.g., nhighest ranked) candidate text representation(s) to natural languageprocessing module 732 for intent deduction, where n is a predeterminedinteger greater than zero. For example, in one example, only the highestranked (n=1) candidate text representation is passed to natural languageprocessing module 732 for intent deduction. In another example, the fivehighest ranked (n=5) candidate text representations are passed tonatural language processing module 732 for intent deduction.

More details on the speech-to-text processing are described in U.S.Utility application Ser. No. 13/236,942 for “Consolidating SpeechRecognition Results,” filed on Sep. 20, 2011, the entire disclosure ofwhich is incorporated herein by reference.

In some examples, STT processing module 730 includes and/or accesses avocabulary of recognizable words via phonetic alphabet conversion module731. Each vocabulary word is associated with one or more candidatepronunciations of the word represented in a speech recognition phoneticalphabet. In particular, the vocabulary of recognizable words includes aword that is associated with a plurality of candidate pronunciations.For example, the vocabulary includes the word “tomato” that isassociated with the candidate pronunciations of /

/ and /

/. Further, vocabulary words are associated with custom candidatepronunciations that are based on previous speech inputs from the user.Such custom candidate pronunciations are stored in STT processing module730 and are associated with a particular user via the user's profile onthe device. In some examples, the candidate pronunciations for words aredetermined based on the spelling of the word and one or more linguisticand/or phonetic rules. In some examples, the candidate pronunciationsare manually generated, e.g., based on known canonical pronunciations.

In some examples, the candidate pronunciations are ranked based on thecommonness of the candidate pronunciation. For example, the candidatepronunciation /

/ is ranked higher than /

/, because the former is a more commonly used pronunciation (e.g., amongall users, for users in a particular geographical region, or for anyother appropriate subset of users). In some examples, candidatepronunciations are ranked based on whether the candidate pronunciationis a custom candidate pronunciation associated with the user. Forexample, custom candidate pronunciations are ranked higher thancanonical candidate pronunciations. This can be useful for recognizingproper nouns having a unique pronunciation that deviates from canonicalpronunciation. In some examples, candidate pronunciations are associatedwith one or more speech characteristics, such as geographic origin,nationality, or ethnicity. For example, the candidate pronunciation /

/ is associated with the United States, whereas the candidatepronunciation /

/ is associated with Great Britain. Further, the rank of the candidatepronunciation is based on one or more characteristics (e.g., geographicorigin, nationality, ethnicity, etc.) of the user stored in the user'sprofile on the device. For example, it can be determined from the user'sprofile that the user is associated with the United States. Based on theuser being associated with the United States, the candidatepronunciation /

/ (associated with the United States) is ranked higher than thecandidate pronunciation /

/ (associated with Great Britain). In some examples, one of the rankedcandidate pronunciations is selected as a predicted pronunciation (e.g.,the most likely pronunciation).

When a speech input is received, STT processing module 730 is used todetermine the phonemes corresponding to the speech input (e.g., using anacoustic model), and then attempt to determine words that match thephonemes (e.g., using a language model). For example, if STT processingmodule 730 first identifies the sequence of phonemes /

/ corresponding to a portion of the speech input, it can then determine,based on vocabulary index 744, that this sequence corresponds to theword “tomato.”

In some examples, STT processing module 730 uses approximate matchingtechniques to determine words in an utterance. Thus, for example, theSTT processing module 730 determines that the sequence of phonemes /

/ corresponds to the word “tomato,” even if that particular sequence ofphonemes is not one of the candidate sequence of phonemes for that word.

Natural language processing module 732 (“natural language processor”) ofthe digital assistant takes the n-best candidate text representation(s)(“word sequence(s)” or “token sequence(s)”) generated by STT processingmodule 730, and attempts to associate each of the candidate textrepresentations with one or more “actionable intents” recognized by thedigital assistant. An “actionable intent” (or “user intent”) representsa task that can be performed by the digital assistant, and can have anassociated task flow implemented in task flow models 754. The associatedtask flow is a series of programmed actions and steps that the digitalassistant takes in order to perform the task. The scope of a digitalassistant's capabilities is dependent on the number and variety of taskflows that have been implemented and stored in task flow models 754, orin other words, on the number and variety of “actionable intents” thatthe digital assistant recognizes. The effectiveness of the digitalassistant, however, also dependents on the assistant's ability to inferthe correct “actionable intent(s)” from the user request expressed innatural language.

In some examples, in addition to the sequence of words or tokensobtained from STT processing module 730, natural language processingmodule 732 also receives contextual information associated with the userrequest, e.g., from I/O processing module 728. The natural languageprocessing module 732 optionally uses the contextual information toclarify, supplement, and/or further define the information contained inthe candidate text representations received from STT processing module730. The contextual information includes, for example, user preferences,hardware, and/or software states of the user device, sensor informationcollected before, during, or shortly after the user request, priorinteractions (e.g., dialogue) between the digital assistant and theuser, and the like. As described herein, contextual information is, insome examples, dynamic, and changes with time, location, content of thedialogue, and other factors.

In some examples, the natural language processing is based on, e.g.,ontology 760. Ontology 760 is a hierarchical structure containing manynodes, each node representing either an “actionable intent” or a“property” relevant to one or more of the “actionable intents” or other“properties.” As noted above, an “actionable intent” represents a taskthat the digital assistant is capable of performing, i.e., it is“actionable” or can be acted on. A “property” represents a parameterassociated with an actionable intent or a sub-aspect of anotherproperty. A linkage between an actionable intent node and a propertynode in ontology 760 defines how a parameter represented by the propertynode pertains to the task represented by the actionable intent node.

In some examples, ontology 760 is made up of actionable intent nodes andproperty nodes. Within ontology 760, each actionable intent node islinked to one or more property nodes either directly or through one ormore intermediate property nodes. Similarly, each property node islinked to one or more actionable intent nodes either directly or throughone or more intermediate property nodes. For example, as shown in FIG.7C, ontology 760 includes a “restaurant reservation” node (i.e., anactionable intent node). Property nodes “restaurant,” “date/time” (forthe reservation), and “party size” are each directly linked to theactionable intent node (i.e., the “restaurant reservation” node).

In addition, property nodes “cuisine,” “price range,” “phone number,”and “location” are sub-nodes of the property node “restaurant,” and areeach linked to the “restaurant reservation” node (i.e., the actionableintent node) through the intermediate property node “restaurant.” Foranother example, as shown in FIG. 7C, ontology 760 also includes a “setreminder” node (i.e., another actionable intent node). Property nodes“date/time” (for setting the reminder) and “subject” (for the reminder)are each linked to the “set reminder” node. Since the property“date/time” is relevant to both the task of making a restaurantreservation and the task of setting a reminder, the property node“date/time” is linked to both the “restaurant reservation” node and the“set reminder” node in ontology 760.

An actionable intent node, along with its linked concept nodes, isdescribed as a “domain.” In the present discussion, each domain isassociated with a respective actionable intent, and refers to the groupof nodes (and the relationships there between) associated with theparticular actionable intent. For example, ontology 760 shown in FIG. 7Cincludes an example of restaurant reservation domain 762 and an exampleof reminder domain 764 within ontology 760. The restaurant reservationdomain includes the actionable intent node “restaurant reservation,”property nodes “restaurant,” “date/time,” and “party size,” andsub-property nodes “cuisine,” “price range,” “phone number,” and“location.” Reminder domain 764 includes the actionable intent node “setreminder,” and property nodes “subject” and “date/time.” In someexamples, ontology 760 is made up of many domains. Each domain sharesone or more property nodes with one or more other domains. For example,the “date/time” property node is associated with many different domains(e.g., a scheduling domain, a travel reservation domain, a movie ticketdomain, etc.), in addition to restaurant reservation domain 762 andreminder domain 764.

While FIG. 7C illustrates two example domains within ontology 760, otherdomains include, for example, “find a movie,” “initiate a phone call,”“find directions,” “schedule a meeting,” “send a message,” and “providean answer to a question,” “read a list,” “providing navigationinstructions,” “provide instructions for a task” and so on. A “send amessage” domain is associated with a “send a message” actionable intentnode, and further includes property nodes such as “recipient(s),”“message type,” and “message body.” The property node “recipient” isfurther defined, for example, by the sub-property nodes such as“recipient name” and “message address.”

In some examples, ontology 760 includes all the domains (and henceactionable intents) that the digital assistant is capable ofunderstanding and acting upon. In some examples, ontology 760 ismodified, such as by adding or removing entire domains or nodes, or bymodifying relationships between the nodes within the ontology 760.

In some examples, nodes associated with multiple related actionableintents are clustered under a “super domain” in ontology 760. Forexample, a “travel” super-domain includes a cluster of property nodesand actionable intent nodes related to travel. The actionable intentnodes related to travel includes “airline reservation,” “hotelreservation,” “car rental,” “get directions,” “find points of interest,”and so on. The actionable intent nodes under the same super domain(e.g., the “travel” super domain) have many property nodes in common.For example, the actionable intent nodes for “airline reservation,”“hotel reservation,” “car rental,” “get directions,” and “find points ofinterest” share one or more of the property nodes “start location,”“destination,” “departure date/time,” “arrival date/time,” and “partysize.”

In some examples, each node in ontology 760 is associated with a set ofwords and/or phrases that are relevant to the property or actionableintent represented by the node. The respective set of words and/orphrases associated with each node are the so-called “vocabulary”associated with the node. The respective set of words and/or phrasesassociated with each node are stored in vocabulary index 744 inassociation with the property or actionable intent represented by thenode. For example, returning to FIG. 7B, the vocabulary associated withthe node for the property of “restaurant” includes words such as “food,”“drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” andso on. For another example, the vocabulary associated with the node forthe actionable intent of “initiate a phone call” includes words andphrases such as “call,” “phone,” “dial,” “ring,” “call this number,”“make a call to,” and so on. The vocabulary index 744 optionallyincludes words and phrases in different languages.

Natural language processing module 732 receives the candidate textrepresentations (e.g., text string(s) or token sequence(s)) from STTprocessing module 730, and for each candidate representation, determineswhat nodes are implicated by the words in the candidate textrepresentation. In some examples, if a word or phrase in the candidatetext representation is found to be associated with one or more nodes inontology 760 (via vocabulary index 744), the word or phrase “triggers”or “activates” those nodes. Based on the quantity and/or relativeimportance of the activated nodes, natural language processing module732 selects one of the actionable intents as the task that the userintended the digital assistant to perform. In some examples, the domainthat has the most “triggered” nodes is selected. In some examples, thedomain having the highest confidence value (e.g., based on the relativeimportance of its various triggered nodes) is selected. In someexamples, the domain is selected based on a combination of the numberand the importance of the triggered nodes. In some examples, additionalfactors are considered in selecting the node as well, such as whetherthe digital assistant has previously correctly interpreted a similarrequest from a user.

User data 748 includes user-specific information, such as user-specificvocabulary, user preferences, user address, user's default and secondarylanguages, user's contact list, and other short-term or long-terminformation for each user. In some examples, natural language processingmodule 732 uses the user-specific information to supplement theinformation contained in the user input to further define the userintent. For example, for a user request “invite my friends to mybirthday party,” natural language processing module 732 is able toaccess user data 748 to determine who the “friends” are and when andwhere the “birthday party” would be held, rather than requiring the userto provide such information explicitly in his/her request.

It should be recognized that in some examples, natural languageprocessing module 732 is implemented using one or more machine learningmechanisms (e.g., neural networks). In particular, the one or moremachine learning mechanisms are configured to receive a candidate textrepresentation and contextual information associated with the candidatetext representation. Based on the candidate text representation and theassociated contextual information, the one or more machine learningmechanism are configured to determine intent confidence scores over aset of candidate actionable intents. Natural language processing module732 can select one or more candidate actionable intents from the set ofcandidate actionable intents based on the determined intent confidencescores. In some examples, an ontology (e.g., ontology 760) is also usedto select the one or more candidate actionable intents from the set ofcandidate actionable intents.

Other details of searching an ontology based on a token string aredescribed in U.S. Utility application Ser. No. 12/341,743 for “Methodand Apparatus for Searching Using An Active Ontology,” filed Dec. 22,2008, the entire disclosure of which is incorporated herein byreference.

In some examples, once natural language processing module 732 identifiesan actionable intent (or domain) based on the user request, naturallanguage processing module 732 generates a structured query to representthe identified actionable intent. In some examples, the structured queryincludes parameters for one or more nodes within the domain for theactionable intent, and at least some of the parameters are populatedwith the specific information and requirements specified in the userrequest. For example, the user says “Make me a dinner reservation at asushi place at 7.” In this case, natural language processing module 732is able to correctly identify the actionable intent to be “restaurantreservation” based on the user input. According to the ontology, astructured query for a “restaurant reservation” domain includesparameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and thelike. In some examples, based on the speech input and the text derivedfrom the speech input using STT processing module 730, natural languageprocessing module 732 generates a partial structured query for therestaurant reservation domain, where the partial structured queryincludes the parameters {Cuisine=“Sushi”} and {Time=“7 μm”}. However, inthis example, the user's utterance contains insufficient information tocomplete the structured query associated with the domain. Therefore,other necessary parameters such as {Party Size} and {Date} is notspecified in the structured query based on the information currentlyavailable. In some examples, natural language processing module 732populates some parameters of the structured query with receivedcontextual information. For example, in some examples, if the userrequested a sushi restaurant “near me,” natural language processingmodule 732 populates a {location} parameter in the structured query withGPS coordinates from the user device.

In some examples, natural language processing module 732 identifiesmultiple candidate actionable intents for each candidate textrepresentation received from STT processing module 730. Further, in someexamples, a respective structured query (partial or complete) isgenerated for each identified candidate actionable intent. Naturallanguage processing module 732 determines an intent confidence score foreach candidate actionable intent and ranks the candidate actionableintents based on the intent confidence scores. In some examples, naturallanguage processing module 732 passes the generated structured query (orqueries), including any completed parameters, to task flow processingmodule 736 (“task flow processor”). In some examples, the structuredquery (or queries) for the m-best (e.g., m highest ranked) candidateactionable intents are provided to task flow processing module 736,where m is a predetermined integer greater than zero. In some examples,the structured query (or queries) for the m-best candidate actionableintents are provided to task flow processing module 736 with thecorresponding candidate text representation(s).

Other details of inferring a user intent based on multiple candidateactionable intents determined from multiple candidate textrepresentations of a speech input are described in U.S. Utilityapplication Ser. No. 14/298,725 for “System and Method for InferringUser Intent From Speech Inputs,” filed Jun. 6, 2014, the entiredisclosure of which is incorporated herein by reference.

Task flow processing module 736 is configured to receive the structuredquery (or queries) from natural language processing module 732, completethe structured query, if necessary, and perform the actions required to“complete” the user's ultimate request. In some examples, the variousprocedures necessary to complete these tasks are provided in task flowmodels 754. In some examples, task flow models 754 include proceduresfor obtaining additional information from the user and task flows forperforming actions associated with the actionable intent.

As described above, in order to complete a structured query, task flowprocessing module 736 needs to initiate additional dialogue with theuser in order to obtain additional information, and/or disambiguatepotentially ambiguous utterances. When such interactions are necessary,task flow processing module 736 invokes dialogue flow processing module734 to engage in a dialogue with the user. In some examples, dialogueflow processing module 734 determines how (and/or when) to ask the userfor the additional information and receives and processes the userresponses. The questions are provided to and answers are received fromthe users through I/O processing module 728. In some examples, dialogueflow processing module 734 presents dialogue output to the user viaaudio and/or visual output, and receives input from the user via spokenor physical (e.g., clicking) responses. Continuing with the exampleabove, when task flow processing module 736 invokes dialogue flowprocessing module 734 to determine the “party size” and “date”information for the structured query associated with the domain“restaurant reservation,” dialogue flow processing module 734 generatesquestions such as “For how many people?” and “On which day?” to pass tothe user. Once answers are received from the user, dialogue flowprocessing module 734 then populates the structured query with themissing information, or pass the information to task flow processingmodule 736 to complete the missing information from the structuredquery.

Once task flow processing module 736 has completed the structured queryfor an actionable intent, task flow processing module 736 proceeds toperform the ultimate task associated with the actionable intent.Accordingly, task flow processing module 736 executes the steps andinstructions in the task flow model according to the specific parameterscontained in the structured query. For example, the task flow model forthe actionable intent of “restaurant reservation” includes steps andinstructions for contacting a restaurant and actually requesting areservation for a particular party size at a particular time. Forexample, using a structured query such as: {restaurant reservation,restaurant=ABC Café, date=3/12/2012, time=7 pm, party size=5}, task flowprocessing module 736 performs the steps of: (1) logging onto a serverof the ABC Caf{tilde over (e)} or a restaurant reservation system suchas OPENTABLE®, (2) entering the date, time, and party size informationin a form on the website, (3) submitting the form, and (4) making acalendar entry for the reservation in the user's calendar.

In some examples, task flow processing module 736 employs the assistanceof service processing module 738 (“service processing module”) tocomplete a task requested in the user input or to provide aninformational answer requested in the user input. For example, serviceprocessing module 738 acts on behalf of task flow processing module 736to make a phone call, set a calendar entry, invoke a map search, invokeor interact with other user applications installed on the user device,and invoke or interact with third-party services (e.g., a restaurantreservation portal, a social networking website, a banking portal,etc.). In some examples, the protocols and application programminginterfaces (API) required by each service are specified by a respectiveservice model among service models 756. Service processing module 738accesses the appropriate service model for a service and generaterequests for the service in accordance with the protocols and APIsrequired by the service according to the service model.

For example, if a restaurant has enabled an online reservation service,the restaurant submits a service model specifying the necessaryparameters for making a reservation and the APIs for communicating thevalues of the necessary parameter to the online reservation service.When requested by task flow processing module 736, service processingmodule 738 establishes a network connection with the online reservationservice using the web address stored in the service model, and send thenecessary parameters of the reservation (e.g., time, date, party size)to the online reservation interface in a format according to the API ofthe online reservation service.

In some examples, natural language processing module 732, dialogue flowprocessing module 734, and task flow processing module 736 are usedcollectively and iteratively to infer and define the user's intent,obtain information to further clarify and refine the user intent, andfinally generate a response (i.e., an output to the user, or thecompletion of a task) to fulfill the user's intent. The generatedresponse is a dialogue response to the speech input that at leastpartially fulfills the user's intent. Further, in some examples, thegenerated response is output as a speech output. In these examples, thegenerated response is sent to speech synthesis processing module 740(e.g., speech synthesizer) where it can be processed to synthesize thedialogue response in speech form. In yet other examples, the generatedresponse is data content relevant to satisfying a user request in thespeech input.

In examples where task flow processing module 736 receives multiplestructured queries from natural language processing module 732, taskflow processing module 736 initially processes the first structuredquery of the received structured queries to attempt to complete thefirst structured query and/or execute one or more tasks or actionsrepresented by the first structured query. In some examples, the firststructured query corresponds to the highest ranked actionable intent. Inother examples, the first structured query is selected from the receivedstructured queries based on a combination of the corresponding speechrecognition confidence scores and the corresponding intent confidencescores. In some examples, if task flow processing module 736 encountersan error during processing of the first structured query (e.g., due toan inability to determine a necessary parameter), the task flowprocessing module 736 can proceed to select and process a secondstructured query of the received structured queries that corresponds toa lower ranked actionable intent. The second structured query isselected, for example, based on the speech recognition confidence scoreof the corresponding candidate text representation, the intentconfidence score of the corresponding candidate actionable intent, amissing necessary parameter in the first structured query, or anycombination thereof.

Speech synthesis processing module 740 is configured to synthesizespeech outputs for presentation to the user. Speech synthesis processingmodule 740 synthesizes speech outputs based on text provided by thedigital assistant. For example, the generated dialogue response is inthe form of a text string. Speech synthesis processing module 740converts the text string to an audible speech output. Speech synthesisprocessing module 740 uses any appropriate speech synthesis technique inorder to generate speech outputs from text, including, but not limited,to concatenative synthesis, unit selection synthesis, diphone synthesis,domain-specific synthesis, formant synthesis, articulatory synthesis,hidden Markov model (HMM) based synthesis, and sinewave synthesis. Insome examples, speech synthesis processing module 740 is configured tosynthesize individual words based on phonemic strings corresponding tothe words. For example, a phonemic string is associated with a word inthe generated dialogue response. The phonemic string is stored inmetadata associated with the word. Speech synthesis processing module740 is configured to directly process the phonemic string in themetadata to synthesize the word in speech form.

In some examples, instead of (or in addition to) using speech synthesisprocessing module 740, speech synthesis is performed on a remote device(e.g., the server system 108), and the synthesized speech is sent to theuser device for output to the user. For example, this can occur in someimplementations where outputs for a digital assistant are generated at aserver system. And because server systems generally have more processingpower or resources than a user device, it is possible to obtain higherquality speech outputs than would be practical with client-sidesynthesis.

Additional details on digital assistants can be found in the U.S.Utility application Ser. No. 12/987,982, entitled “Intelligent AutomatedAssistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No.13/251,088, entitled “Generating and Processing Task Items ThatRepresent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosuresof which are incorporated herein by reference.

With reference back to FIG. 7A, digital assistant module 726 furtherincludes audio processing module 770 and latency management module 780.Audio processing module 770 is configured to analyze a stream of audioreceived by digital assistant system 700 (e.g., at I/O processing module728 and via microphone 213). In some examples, audio processing module770 is configured to analyze the stream of audio to identify whichportions contain user speech and which portions do not contain userspeech. For example, audio processing module 770 divides the stream ofaudio into a sequence of overlapping audio frames. Each audio frame hasa predetermined duration (e.g., 10 ms). Audio processing module 770analyzes the audio features of each audio frame (e.g., using audioand/or speech models) to determine whether or not each audio framecontains user speech. The analyzed audio features can include timedomain and/or frequency domain features. Time domain features include,for example, zero-crossing rates, short-time energy, spectral energy,spectral flatness, autocorrelation, or the like. Frequency domainfeatures include, for example, mel-frequency cepstral coefficients,linear predictive cepstral coefficients, mel-frequency discrete waveletcoefficients, or the like. In some examples, audio processing module 770provides audio frame information indicating which audio frames of thestream of audio contain user speech and which audio frames of the streamof audio do not contain user speech to other components of digitalassistant module 726.

In some examples, latency management module 780 receives the audio frameinformation from audio processing module 770. Latency management module780 uses this information to control the timing of various digitalassistant processes to reduce latency. For example, latency managementmodule 780 uses the audio frame information to detect pauses orinterruptions in user speech in the stream of audio. In addition, theduration of each pause or interruption can be determined. In someexamples, latency management module 780 applies one or morepredetermined rules to determine whether a first portion of the streamof audio satisfies a predetermined condition. In some examples, thepredetermined condition includes the condition of detecting, in thefirst portion of the stream of audio, an absence of user speech (e.g., apause) for longer than a first predetermined duration (e.g., 50 ms, 75ms, or 100 ms). In response to determining that the first portion of thestream of audio satisfies a predetermined condition, latency managementmodule 780 initiates performance of natural language processing (e.g.,at natural language processing module 732), task flow processing (e.g.,at task flow processing module 736 or 836), and/or speech synthesis(e.g., at speech synthesis processing module 740) based on the userutterance contained in the first portion of the stream of audio. In someexamples, latency management module 780 initiates performance of theseprocesses while causing the digital assistant system to continuereceiving a second portion of the stream of audio.

In some examples, latency management module 780 is configured to detecta speech end-point condition. Specifically, after determining that thefirst portion of the stream of audio satisfies a predeterminedcondition, latency management module 780 determines whether a speechend-point condition is detected. In some examples, latency managementmodule 780 uses the audio frame information to determine whether aspeech end-point condition is detected. For example, detecting thespeech end-point condition can include detecting, in the second portionof the stream of audio, an absence of user speech for greater than asecond predetermined duration (e.g., 600 ms, 700 ms, or 800 ms). Thesecond predetermined duration is longer than the first predeterminedduration. In some examples, detecting the speech end-point conditionincludes detecting a predetermined type of non-speech input from theuser. For example, the predetermined type of non-speech input can be auser selection of a button (e.g., “home” or menu button 304) of theelectronic device or an affordance displayed on the touch screen (e.g.,touch screen 212) of the electronic device. In response to determiningthat a speech end-point condition is detected, latency management module780 causes results generated by task flow processing module 736 or 836,dialogue flow processing module 734, and/or speech synthesis processingmodule 740 to be presented to the user. In some examples, the resultsinclude spoken dialogue. In some examples, latency management module 780prevents the generated results from being presented to the user prior todetermining that a speech end-point condition is detected.

In some examples, latency management module 780 determines that a speechend-point condition is not detected. Instead, latency management module780 detects additional speech in the second portion of the stream ofaudio. Specifically, for example, the additional speech is acontinuation of the utterance in the first portion of the stream ofaudio. In these examples, latency management module 780 re-initiatesperformance of natural language processing, task flow processing, and/orspeech synthesis based on the user utterance across the first and secondportions of the stream of audio. The latency reducing functions oflatency management module 780 are described in greater detail below withreference to FIGS. 9 and 10.

FIG. 8 is a block diagram illustrating a portion of digital assistantmodule 800, according to various examples. In particular, FIG. 8 depictscertain components of digital assistant module 800 that can enablerobust operation of a digital assistant, according to various examples.More specifically, the components depicted in digital assistant module800 can function to evaluate multiple candidate task flows correspondingto a user utterance and improve the robustness and reliability of taskflow processing. For simplicity, only a portion of digital assistantmodule 800 is depicted. It should be recognized that digital assistantmodule 800 can include additional components. For example, digitalassistant module 800 can be similar or substantially identical todigital assistant module 726 and can reside in memory 702 of digitalassistant system 700.

As shown in FIG. 8, STT processing module 830 receives a user utterance(e.g., via I/O processing module 728). STT processing module 830 issimilar or substantially identical to STT processing module 730. In anillustrative example, the received user utterance is “Directions toFidelity Investments.” STT processing module 830 performs speechrecognition on the user utterance to determine a plurality of candidatetext representations. Each candidate text representation of theplurality of candidate text representations corresponds to the userutterance. STT processing module 830 further determines an associatedspeech recognition confidence score for each candidate textrepresentation. In some examples, the determined plurality of candidatetext representations are the n-best candidate text representationshaving the n-highest speech recognition confidence scores. In thepresent example, STT processing module 830 determines three candidatetext representations for the user utterance, which include “Directionsto Fidelity Investments,” “Directions to deli restaurants,” and“Directions to Italian restaurants.” The candidate text representation“Directions to Fidelity Investments” can have the highest speechrecognition confidence score and the candidate text representations“Directions to deli restaurants” and “Directions to Italian restaurants”can have lower speech recognition confidence scores.

STT processing module 830 provides the three candidate textrepresentations and the associated speech recognition confidence scoresto natural language processing module 832. Natural language processingmodule 832 can be similar or substantially identical to natural languageprocessing module 732. Based on the three candidate textrepresentations, natural language processing module 832 determinescorresponding candidate user intents. Each candidate user intent isdetermined from a respective candidate text representation. Naturallanguage processing module 832 further determines an associated intentconfidence score for each candidate user intent.

Determining a candidate user intent from a respective candidate textrepresentation includes, for example, parsing the respective candidatetext representation to determine a candidate domain and candidate parseinterpretations for the candidate text representation. The determinedcandidate user intent can be represented in the form of a structuredquery based on the determined candidate domain and parseinterpretations. For instance, in the present example, natural languageprocessing module 832 parses the candidate text interpretation“Directions to Fidelity Investments” to determine that a candidatedomain is “get directions.” In addition, natural language processingmodule 832 recognizes that “Fidelity Investments” is an entry in theuser's contact list and thus interprets it as a person/entity of thecontact list (contacts=“Fidelity Investment”). Thus, a first candidateuser intent determined for the candidate text interpretation “Directionsto Fidelity Investments” can be represented by the structured query {Getdirections, location=search(contacts=“Fidelity Investments”)}. In someexamples, natural language processing module 832 can also interpret“Fidelity Investments” as a business. Thus, a second candidate userintent determined for the candidate text interpretation “Directions toFidelity Investments” can be represented by the structured query {Getdirections, location=search(contacts=“Fidelity Investments”)}.

The candidate text representations “Directions to deli restaurants” and“Directions to Italian restaurants” can similarly be parsed by naturallanguage processing module 832 to determine respective candidate userintents. Specifically, natural language processing module 832 caninterpret “deli” and “Italian” as types of cuisine associated withrestaurants. Thus, candidate user intents determined for these candidatetext interpretations can be represented by the structured queries {Getdirections, location=search(restaurant, cuisine=“deli”)} and {Getdirections, location=search(restaurant, cuisine=“Italian”)},respectively.

Therefore, in the present example, natural language processing module832 determines four candidate user intents from the three candidate textrepresentations. The four candidate user intents are represented by thefollowing respective structured queries:

-   -   1. {Get directions, location=search(contacts=“Fidelity        Investments”)}    -   2. {Get directions, location=search(business=“Fidelity        Investments”)}    -   3. {Get directions, location=search(restaurant, cuisine=“deli”)}    -   4. {Get directions, location=search(restaurant,        cuisine=“Italian”)}        Each of the four candidate user intents has an associated intent        confidence score. In this example, the four candidate user        intents are arranged in decreasing order of intent confidence        scores, with the first candidate user intent having the highest        intent confidence score and the fourth candidate user intent        having the lowest intent confidence score. Although in the        present example, each of the determined candidate user intents        has the same inferred domain (“get directions”), it should be        recognized that, in other examples, the determined candidate        user intents can include a plurality of different inferred        domains.

Natural language processing module 832 provides the four candidate userintents to task flow processing module 836. For example, the structuredqueries for the four candidate user intents are provided to task flowprocessing module 836. In addition, the associated speech recognitionconfidence scores and intent confidence scores are provided to task flowprocessing module 836. In some examples, task flow processing module 836is similar or substantially identical to task flow processing module736.

In some examples, task flow manager 838 of task flow processing module836 initially only selects one of the four candidate user intents forprocessing. If task flow manager 838 determines that the initiallyselected candidate user intent cannot be successfully processed throughtask flow processing module 836, task flow manager 838 selects anothercandidate user intent for processing. For example, the first candidateuser intent having the highest intent confidence score is initiallyselected. Specifically, in the present example, the first candidate userintent represented by the structured query {Get directions,location=search(contacts=“Fidelity Investments”)} is selected by taskflow manager 838. Task flow manager 838 maps the first candidate userintent to a corresponding first candidate task flow (e.g., firstcandidate task flow 842). Notably, the structured query for the firstcandidate user intent is incomplete because it does not contain anyvalue for the “location” property. As a result, the “location” taskparameter in the corresponding first candidate task flow can be missinga required value for performing the task of getting directions to alocation corresponding to “Fidelity Investments.” In this example, thefirst candidate task flow includes procedures for resolving the“location” task parameter. Specifically, the first candidate task flowincludes procedures for searching the “Fidelity Investments” entry ofthe user's contact list to obtain a corresponding value (e.g., addressvalue) for the “location” task parameter.

In some examples, task flow manager 838 determines a corresponding firsttask flow score for the first candidate task flow. The first task flowscore can represent the likelihood that the corresponding candidate taskflow is the correct candidate task flow to perform given the userutterance. In some examples, the first task flow score is based on afirst flow parameter score. The first flow parameter score can representa confidence of resolving one or more flow parameters for the firstcandidate task flow. In some examples, the first task flow score isbased on any combination of a speech recognition confidence score forthe corresponding candidate text representation “Directions to FidelityInvestments,” an intent confidence score for the first candidate userintent, and a first task parameter score.

Task flow resolver 840 determines the first flow parameter score byattempting to resolve the missing “location” flow parameter for thefirst candidate task flow. In the present example, task flow resolver840 attempts to search the “Fidelity Investments” entry of the user'scontact list to obtain a value (e.g., address value) for the “location”flow parameter. If task flow resolver 840 successfully resolves the“location” flow parameter by obtaining a value from the “FidelityInvestments” entry of the user's contact list, task flow resolver 840can determine a higher value for the first flow parameter score.However, if task flow resolver 840 is unable to successfully resolve the“location” flow parameter (e.g., no address value is found in the“Fidelity Investments” entry of the user's contact list), task flowresolver 840 can determine a lower value for the first flow parameterscore. Thus, the first flow parameter score can be indicative of whetherone or more flow parameters of the first candidate task flow (e.g., the“location” parameter) can be successfully resolved. In some examples,task flow resolver 840 can utilize context data to attempt to resolvemissing flow parameters.

In some examples, task flow manager 838 determines whether the firsttask flow score satisfies a predetermined criterion (e.g., greater thana predetermined threshold level). In the example where task flowresolver 840 successfully resolves the “location” flow parameter, thefirst flow parameter score can be sufficiently high to enable the firsttask flow score to satisfy the predetermined criterion. In this example,task flow manager 838 determines that the first task flow scoresatisfies the predetermined criterion and in response, task flow manager838 executes the corresponding first candidate task flow withoutprocessing the remaining three candidate user intents. In the presentexample, executing the first candidate task flow can include searchingfor directions to the address obtained from the “Fidelity Investments”entry of the user's contact list and displaying the directions to theuser on the electronic device. In some examples, executing the firstcandidate task flow further includes generating a dialogue text that isresponsive to the user utterance and outputting a spoken representationof the dialogue text. For example, the outputted spoken representationof the dialogue text can be “OK, here are directions to FidelityInvestments.”

In the alternative example where task flow resolver 840 is unable tosuccessfully resolve the “location” flow parameter, the first flowparameter score can be sufficiently low to result in the first task flowscore not satisfying the predetermined criterion. In this example, taskflow manager 838 forgoes executing the corresponding first candidatetask flow and proceeds to select a second candidate user intent forprocessing. For example, the second candidate user intent having thesecond highest intent confidence score can be selected. In the presentexample, the selected second candidate user intent is represented by thesecond structured query {Get directions,location=search(business=“Fidelity Investments”)}.

Task flow manager 838 maps the second candidate user intent to acorresponding second candidate task flow (e.g., second candidate taskflow 844). The second candidate task flow is processed in a similarmanner as the first candidate task flow. Specifically, task flowresolver 840 determines a second flow parameter score for the secondcandidate task flow by attempting to resolve one or more missing taskparameters for the second candidate task flow. For example, task flowresolver 840 attempts to search one or more “business” data sources(e.g., a business directory) to obtain an address value corresponding tothe business “Fidelity Investments.” The determined second flowparameter score is based on whether task flow resolver 840 cansuccessfully resolve the “location” flow parameter by searching the“business” data source. Based on the second flow parameter score, taskflow manager 838 determines a second task flow score for the secondcandidate task flow. In some examples, the second task flow score isfurther based on the speech recognition confidence score for thecorresponding candidate text representation “Directions to FidelityInvestments” and/or the intent confidence score for the second candidateuser intent. If task flow manager 838 determines that the second taskflow score satisfies the predetermined criterion, then the secondcandidate task flow is executed. However, if task flow manager 838determines that the second task flow score does not satisfy thepredetermined criterion, then task flow manager 838 forgoes executingthe second candidate task flow and continues to evaluate the thirdcandidate user intent (and if necessary, the fourth candidate userintent) until a corresponding candidate task flow is determined to havean associated task flow score that satisfies the predeterminedcriterion.

Although in the examples described above, task flow processing module836 evaluates the candidate user intents serially to determine acandidate task flow having an associated task flow score that satisfiesthe predetermined criterion, it should be recognized that, in otherexamples, task flow processing module 836 can evaluate the candidateuser intents in parallel. In these examples, task flow manager 838 mapseach of the four candidate user intents to four respective candidatetask flows. Task flow manager 838 then determines a respective task flowscore for each candidate task flow. As discussed above, task flowprocessing module 836 determines each task flow score based on thespeech recognition confidence score for the respective candidate textrepresentation, the intent confidence score for the respective candidateuser intent, the respective task parameter score, or any combinationthereof. Task flow processing module 836 determines each respective taskparameter score by attempting to resolve one or more missing taskparameters for the respective candidate task flow. Based on thedetermined task flow scores for the four candidate task flows, task flowmanager 838 ranks the four candidate task flows and selects the highestranking candidate task flow. Task flow manager 838 then executes theselected candidate task flow.

In some examples, the highest ranking candidate task flow is thecandidate task flow with the highest task flow score. In the presentexample, the highest ranking candidate task flow can correspond to thesecond candidate user intent represented by the structured query {Getdirections, location=search(business=“Fidelity Investments”)}. Thus, inthe present example, Task flow manager 838 can execute the secondcandidate user intent, which can include obtaining directions to a“Fidelity Investments” address obtained by searching one or morebusiness data sources and presenting the directions to the user (e.g.,by displaying a map with the directions).

It should be appreciated that, in some examples, the selected highestranking candidate task flow need not correspond to the candidate userintent with the highest intent confidence score. For instance, in thepresent example, the selected second candidate task flow corresponds tothe second candidate user intent (e.g., represented by the structuredquery {Get directions, location=search(business=“FidelityInvestments”)}), which does not have the highest intent confidencescore. Further, in some examples, the selected highest ranking candidatetask flow need not correspond to the candidate text representations withthe highest speech recognition confidence score. Because the task flowscore can be based on a combination of speech recognition confidencescores, intent confidence scores, and flow parameter scores, using thetask flow scores to select a suitable candidate task flow can enable acandidate task flow that represents an optimization of speechrecognition, natural language processing, and task flow processing to beselected. As a result, the selected candidate task flow can be morelikely to coincide with the user's actual desired goal for providing theuser utterance and less likely to fail (e.g., causes a fatal error)during execution.

FIGS. 9 and 10 are timelines 900 and 1000 illustrating the timing forlow-latency operation of a digital assistant, according to variousexamples. In some examples, the timing for low-latency operation of adigital assistant is controlled using a latency management module (e.g.,latency management module 780) of a digital assistant module (e.g.,digital assistant module 726). FIGS. 9 and 10 are described withreferences to digital assistant system 700 of FIGS. 7A and 7B.

As shown in FIG. 9, digital assistant system 700 begins receiving streamof audio 902 at first time 904. For example, digital assistant system700 begins receiving stream of audio 902 at first time 904 in responseto receiving user input that invokes digital assistant system 700. Inthis example, stream of audio 902 is continuously received from firsttime 904 to third time 910. Specifically, a first portion of stream ofaudio 902 is received from first time 904 to second time 908 and asecond portion of stream of audio 902 is received from second time 908to third time 910. As shown, the first portion of stream of audio 902includes user utterance 903.

In some examples, digital assistant system 700 performs speechrecognition as stream of audio 902 is being received. For example,latency management module 780 causes STT processing module 730 tobeginning performing speech recognition in real-time as stream of audio902 is being received. STT processing module 730 determines one or morefirst candidate text representations for user utterance 903.

Latency management module 780 determines whether the first portion ofstream of audio 902 satisfies a predetermined condition. For example,the predetermined condition can include the condition of detecting anabsence of user speech in the first portion of stream of audio 902 forlonger than a first predetermined duration (e.g., 50 ms, 75 ms, or 100ms). It should be appreciated that, in other examples, the predeterminedcondition can include other conditions associated with the first portionof stream of audio 902. In the present example, as shown in FIG. 9, thefirst portion of stream of audio 902 contains an absence of user speechbetween first intermediate time 906 and second time 908. If latencymanagement module 780 determines that this absence of user speechbetween first intermediate time 906 and second time 908 satisfies thepredetermined condition (e.g., duration 912 is longer than the firstpredetermined duration), latency management module 780 causes therelevant components of digital assistant system 700 to initiate asequence of processes that include natural language processing, taskflow processing, dialogue flow processing, speech synthesis, or anycombination thereof. Specifically, in the present example, in responseto determining that the first portion of stream of audio 902 satisfiesthe predetermined condition, latency management module 780 causesnatural language processing module 732 to begin performing, at secondtime 908, natural language processing on the one or more first candidatetext representations. This can be advantageous because natural languageprocessing, task flow processing, dialogue flow processing, or speechsynthesis can be at least partially completed between second time 908and third time 910 while digital assistant system 700 is awaiting thedetection of a speech end-point condition. As a result, less processingcan be required after the speech end-point condition is detected, whichcan reduce the response latency of digital assistant system 700.

As discussed above, latency management module 780 causes one or more ofnatural language processing, task flow processing, dialogue flowprocessing, and speech synthesis to be performed while the secondportion of stream of audio 902 is being received between second time 908and third time 910. Specifically, between second time 908 and third time910, latency management module 780 causes natural language processingmodule 732 to determine one or more candidate user intents for userutterance 903 based on the one or more first candidate textrepresentations. In some examples, latency management module 780 alsocauses task flow processing module 736 (or 836) to determine (e.g., atleast partially between second time 908 and third time 910) one or morerespective candidate task flows for the one or more candidate userintents and to select (e.g., at least partially between second time 908and third time 910) a first candidate task flow from the one or morecandidate task flows. In some examples, latency management module 780further causes task flow processing module 736 (or 836) to execute(e.g., at least partially between second time 908 and third time 910)the selected first candidate task flow without providing an output to auser of digital assistant system 700 (e.g., without displaying anyresult or outputting any speech/audio on the user device).

In some examples, executing the first candidate task flow includesgenerating a text dialogue that is responsive to user utterance 903 andgenerating a spoken representation of the text dialogue. In theseexamples, latency management module 780 further causes dialogue flowprocessing module 734 to generate (e.g., at least partially betweensecond time 908 and third time 910) the text dialogue and causes speechsynthesis processing module 740 to generate (e.g., at least partiallybetween second time 908 and third time 910) the spoken representation ofthe text dialogue.

In some examples, speech synthesis processing module 740 receives arequest (e.g., from task flow processing module 736 or dialogue flowprocessing module 734) to generate the spoken representation of the textdialogue. In response to receiving the request, speech synthesisprocessing module 740 can determine (e.g., at least partially betweensecond time 908 and third time 910) whether the memory (e.g., memory202, 470, or 702) of the electronic device (e.g., server 106, device104, device 200, or system 700) stores an audio file having a spokenrepresentation of the text dialogue. In response to determining that thememory of the electronic device does store an audio file having a spokenrepresentation of the text dialogue, speech synthesis processing module740 awaits detection of an end-point condition before playing the storedaudio file. In response to determining that the memory of the electronicdevice does not store an audio file having a spoken representation ofthe text dialogue, speech synthesis processing module 740 generates anaudio file having a spoken representation of the text dialogue andstores the audio file in the memory. In some examples, generating andstoring the audio file are at least partially performed between secondtime 908 and third time 910. After storing the audio file, speechsynthesis processing module 740 awaits detection of a speech end-pointcondition before playing the stored audio file.

Latency management module 780 determines whether a speech end-pointcondition is detected between second time 908 and third time 910. Forexample, detecting the speech end-point condition can include detecting,in the second portion of stream of audio 902, an absence of user speechfor longer than a second predetermined duration (e.g., 600 ms, 700 ms,or 800 ms). It should be recognized that, in other examples, otherspeech end-point conditions can be implemented. In the present example,the second portion of stream of audio 902 between second time 908 andthird time 910 does not contain any user speech. In addition, duration914 between second time 908 and third time 910 is longer than the secondpredetermined duration. Thus, in this example, a speech end-pointcondition is detected between second time 908 and third time 910. Inresponse to determining that a speech end-point condition is detectedbetween the second time and the third time, latency management module780 causes digital assistant system 700 to present (e.g., at fourth time1014) the results obtained from executing the first candidate task flow.For example, the results can be displayed on a display of the electronicdevice. In some examples, latency management module 780 causes output ofthe spoken representation of the text dialogue to the user by causing arespective stored audio file to be played.

Because at least a portion of natural language processing, task flowprocessing, dialogue flow processing, and speech synthesis is performedprior to detecting the speech end-point condition, less processing canbe required after the speech end-point condition is detected, which canreduce the response latency of digital assistant system 700.Specifically, the results obtained from executing the first candidatetask flow can be presented more quickly after the speech end-pointcondition is detected.

In other examples, a speech end-point condition is not detected betweensecond time 908 and third time 910. For example, with reference totimeline 1000 in FIG. 10, stream of audio 1002 contains user speechbetween second time 1008 and third time 1012. Thus, in this example, aspeech end-point condition is not detected between second time 1008 andthird time 1012. Timeline 1000 of FIG. 10 from first time 1004 to secondtime 1008 can be similar or substantially identical to timeline 900 ofFIG. 9 from first time 904 to second time 908. In particular, the firstportion of stream of audio 1002 containing user utterance 1003 isreceived from first time 1004 to second time 1008. Latency managementmodule 780 determines that the absence of user speech between firstintermediate time 1006 and second time 1008 satisfies the predeterminedcondition (e.g., duration 1018 is longer than the first predeterminedduration) and in response, latency management module 780 causes therelevant components of digital assistant system 700 to initiate, for thefirst time, a sequence of processes that include natural languageprocessing, task flow processing, dialogue flow processing, speechsynthesis, or any combination thereof. Specifically, in response todetermining that the first portion of stream of audio 1002 satisfies thepredetermined condition, latency management module 780 causes naturallanguage processing module 732 to begin performing, at second time 1008,natural language processing on one or more first candidate textrepresentations of user utterance 1003 in the first portion of stream ofaudio 1002.

Timeline 1000 of FIG. 10 differs from timeline 900 of FIG. 9 in thatuser utterance 1003 continues from the first portion of stream of audio1002 (between first time 1004 and second time 1008) to the secondportion of stream of audio 1002 (between second time 1008 and third time1012) and stream of audio 1002 further extends from third time 1012 tofourth time 1014. In this example, latency management module 780determines that a speech end-point condition is not detected betweensecond time 1008 and third time 1012 (e.g., due to detecting userspeech) and in response, latency management module 780 causes digitalassistant system 700 to forgo presentation of any results obtained fromperforming task flow processing between second time 1008 and third time1012. In other words, the natural language processing, task flowprocessing, dialogue flow processing, or speech synthesis performedbetween second time 1008 and third time 1012 can be discarded upondetecting user speech in the second portion of stream of audio 1002. Inaddition, upon detecting user speech in the second portion of stream ofaudio 1002, latency management module 780 causes digital assistantsystem 700 to process the user speech (continuation of user utterance1003) in the second portion of stream of audio 1002. Specifically,latency management module 780 causes STT processing module 730 toperform speech recognition on the second portion of stream of audio 1002and determine one or more second candidate text representations. Eachcandidate text representation of the one or more second candidate textrepresentations is a candidate text representation of user utterance1003 across the first and second portions of stream of audio 1002 (e.g.,from first time 1004 to third time 1012). Further, upon detecting userspeech in the second portion of stream of audio 1002, latency managementmodule 780 causes digital assistant system 700 to continue receivingstream of audio 1002 from third time 1012 to fourth time 1014.Specifically, a third portion of stream of audio 1002 is received fromthird time 1012 and fourth time 1014.

The second and third portions of stream of audio 1002 are processed in asimilar manner as the first and second portions of stream of audio 902,described above with reference to FIG. 9. In particular, latencymanagement module 780 determines whether the second portion of stream ofaudio 1002 satisfies the predetermined condition (e.g., absences of userspeech for longer than a first predetermined duration). In the presentexample, as shown in FIG. 10, the second portion of stream of audio 1002contains an absence of user speech between second intermediate time 1010and third time 1012. If latency management module 780 determines thatthis absence of user speech between second intermediate time 1010 andthird time 1012 satisfies the predetermined condition (e.g., duration1020 is longer than the first predetermined duration), latencymanagement module 780 causes the relevant components of digitalassistant system 700 to initiate, for a second time, a sequence ofprocesses that include natural language processing, task flowprocessing, dialogue flow processing, speech synthesis, or anycombination thereof. Specifically, in the present example, in responseto determining that the second portion of stream of audio 1002 satisfiesthe predetermined condition, latency management module 780 causesnatural language processing module 732 to begin performing, at thirdtime 1012, natural language processing on the one or more secondcandidate text representations.

As discussed above, latency management module 780 causes one or more ofnatural language processing, task flow processing, dialogue flowprocessing, and speech synthesis to be performed between third time 1012and fourth time 1014. In particular, between third time 1012 and fourthtime 1014, latency management module 780 causes natural languageprocessing module 732 to determine, based on the one or more secondcandidate text representations, one or more second candidate userintents for user utterance 1003 in the first and second portions ofstream of audio 1002. In some examples, latency management module 780causes task flow processing module 736 (or 836) to determine (e.g., atleast partially between third time 1012 and fourth time 1014) one ormore respective second candidate task flows for the one or more secondcandidate user intents and to select (e.g., at least partially betweenthird time 1012 and fourth time 1014) a second candidate task flow fromthe one or more second candidate task flows. In some examples, latencymanagement module 780 further causes task flow processing module 736 (or836) to execute (e.g., at least partially between third time 1012 andfourth time 1014) the selected second candidate task flow withoutproviding an output to a user of the digital assistant system (e.g.,without displaying any result or outputting any speech/audio on the userdevice).

Latency management module 780 determines whether a speech end-pointcondition is detected between third time 1012 and fourth time 1014. Inthe present example, the third portion of stream of audio 1002 betweenthird time 1012 and fourth time 1014 does not contain any user speech.In addition, duration 1022 between third time 1012 and fourth time 1014is longer than the second predetermined duration. Thus, in this example,a speech end-point condition is detected between third time 1012 andfourth time 1014. In response to determining that a speech end-pointcondition is detected between third time 1012 and fourth time 1014,latency management module 780 causes digital assistant system 700 topresent (e.g., at fourth time 1014) the results obtained from executingthe first candidate task flow. For example, the results can be displayedon a display of the electronic device. In other examples, presenting theresults includes outputting spoken dialogue that is responsive to userutterance 1003. In these examples, the spoken dialogue can be at leastpartially generated between third time 1012 and fourth time 1014.

4. Process for Operating a Digital Assistant

FIGS. 11A-11B illustrate process 1100 for operating a digital assistant,according to various examples. Some aspects of process 1100 relate tolow-latency operation of a digital assistant. In addition, some aspectsof process 1100 relate to more reliable and robust operation of adigital assistant. Process 1100 is performed, for example, using one ormore electronic devices implementing a digital assistant. In someexamples, process 1100 is performed using a client-server system (e.g.,system 100), and the blocks of process 1100 are divided up in any mannerbetween the server (e.g., DA server 106) and a client device (userdevice 104). In other examples, the blocks of process 1100 are dividedup between the server and multiple client devices (e.g., a mobile phoneand a smart watch). Thus, while portions of process 1100 are describedherein as being performed by particular devices of a client-serversystem, it will be appreciated that process 1100 is not so limited. Inother examples, process 1100 is performed using only a client device(e.g., user device 104) or only multiple client devices. In process1100, some blocks are, optionally, combined, the order of some blocksis, optionally, changed, and some blocks are, optionally, omitted. Insome examples, additional steps may be performed in combination withprocess 1100.

At block 1102, a stream of audio (e.g., stream of audio 902 or 1002) isreceived (e.g., at I/O processing module 728 via microphone 213). Thestream of audio is received, for example, by activating a microphone(e.g., microphone 213) of the electronic device (e.g., user device 104)and initiating the collection of audio data via the microphone.Activation of the microphone and initiating collection of audio data canbe performed in response to detecting a predetermined user input. Forexample, detecting the activation of a “home” affordance of theelectronic device (e.g., by the user pressing and holding theaffordance) can invoke the digital assistant and initiate the receivingof the stream of audio. In some examples, the stream of audio is acontinuous stream of audio data. The stream of audio data can becontinuously collected and stored in a buffer (e.g., buffer of audiocircuitry 210).

In some examples, block 1102 is performed in accordance with blocks 1104and 1106. Specifically, in these examples, the stream of audio isreceived across two time intervals. At block 1104, a first portion ofthe stream of audio is received from a first time (e.g., first time 904or 1004) to a second time (e.g., second time 908 or 1008). The firstportion of the stream of audio contains, for example, a user utterance(user utterance 903 or 1003). At block 1106, a second portion of thestream of audio is received from the second time (e.g., second time 908or 1008) to a third time (e.g., third time 910 or 1012). The first time,second time, and third time are each specific points of time. The secondtime is after the first time and the third time is after the secondtime. In some examples, the stream of audio is continuously receivedfrom the first time through to the third time, wherein the first portionof the stream of audio is continuously received from the first time tothe second time and the second portion of the stream of audio iscontinuously received from the second time to the third time.

At block 1108, a plurality of candidate text representations of the userutterance are determined (e.g., using STT processing module 730). Theplurality of candidate text representations are determined by performingspeech recognition on the stream of audio. Each candidate textrepresentation is associated with a respective speech recognitionconfidence score. The speech recognition confidence scores can indicatethe confidence that a particular candidate text representation is thecorrect text representation of the user utterance. In addition, thespeech recognition confidence scores can indicate the confidence of anydetermined word in a candidate text representation of the plurality ofcandidate text representations. In some examples, the plurality ofcandidate text representations are the n-best candidate textrepresentations having the n-highest speech recognition confidencescores.

In some examples, block 1108 is performed in real-time as the userutterance is being received at block 1104. In some examples, speechrecognition is performed automatically upon receiving the stream ofaudio. In particular, words of the user utterance are decoded andtranscribed as each portion of the user utterance is received. In theseexamples, block 1108 is performed prior to block 1110. In otherexamples, block 1108 is performed after block 1110 (e.g., performed inresponse to determining that the first portion of the stream of audiosatisfies a predetermined condition).

At block 1110, a determination is made (e.g., using latency managementmodule 780) as to whether the first portion of the stream of audiosatisfies a predetermined condition. In some examples, the predeterminedcondition is a condition based on one or more audio characteristics ofthe stream of audio. The one or more audio characteristics include, forexample, one or more time domain and/or frequency domain features of thestream of audio. Time domain features include, for example,zero-crossing rates, short-time energy, spectral energy, spectralflatness, autocorrelation, or the like. Frequency domain featuresinclude, for example, mel-frequency cepstral coefficients, linearpredictive cepstral coefficients, mel-frequency discrete waveletcoefficients, or the like.

In some examples, the predetermined condition includes the condition ofdetecting, in the first portion of the stream of audio, an absence ofuser speech for longer than a first predetermined duration after theuser utterance. Specifically, process 1100 can continuously monitor thefirst portion of the stream of audio (e.g., from the first time to thesecond time) and determine the start time and end time of the userutterances (e.g., using conventional speech detection techniques). If anabsence of user speech is detected in the first portion of the stream ofaudio for longer than a first predetermined duration (e.g., 50 ms, 75ms, or 100 ms) after the end time of the user utterance, it can bedetermined that the first portion of the stream of audio satisfies thepredetermined condition.

In some examples, the presence or absence of user speech is detectedbased on audio energy level (e.g., energy level of the stream of audiowithin a frequency range corresponding to human speech, such as 50-500Hz). In these examples, the predetermined condition includes thecondition of detecting, in the first portion of the stream of audio, anaudio energy level that is less than a predetermined threshold energylevel for longer than a first predetermined duration after the end timeof the user utterance.

In some examples, the predetermined condition includes a condition thatrelates to a linguistic characteristic of the user utterance. Forexample, the plurality of candidate text representations of block 1108can be analyzed to determine whether an end-of-sentence condition isdetected in the one or more candidate text representations. In someexamples, the end-of-sentence condition is detected if the endingportions of the one or more candidate text representations match apredetermined sequence of words. In some examples, a language model isused to detect an end-of-sentence condition in the one or more candidatetext representations.

In response to determining that the first portion of the stream of audiosatisfies a predetermined condition, one or more of the operations ofblocks 1112-1126 are performed. In particular, one or more of theoperations of blocks 1112-1126 are performed automatically (e.g.,without further input from the user) in response to determining that thefirst portion of the stream of audio satisfies a predeterminedcondition. Further, in response to determining that the first portion ofthe stream of audio satisfies a predetermined condition, one or more ofthe operations of blocks 1112-1126 are at least partially performedbetween the second time (e.g., second time 908 or 1008) and the thirdtime (e.g., third time 910 or 1012) (e.g., while the second portion ofthe stream of audio is received at block 1106).

In response to determining that the first portion of the stream of audiodoes not satisfy a predetermined condition, block 1110 continues tomonitor the first portion of the stream of audio (e.g., withoutperforming blocks 1112-1126) until it is determined that thepredetermined condition is satisfied by the first portion of the streamof audio.

Determining whether the first portion of the stream of audio satisfies apredetermined condition and performing, at least partially between thesecond time and the third time, one or more of the operations of blocks1112-1126 in response to determining that the first portion of thestream of audio satisfies a predetermined condition can reduce theresponse latency of the digital assistant on the electronic device. Inparticular, the electronic device can at least partially complete theseoperations while waiting for the speech end-point condition to bedetected. This can enhance operability of the electronic device byreducing the operations needed to be performed after detecting thespeech end-point condition. In turn, this can reduce the overall latencybetween receiving the user utterance (block 1104) and presenting theresults to the user (block 1130).

At block 1112, a plurality of candidate user intents for the userutterance are determined (e.g., using natural language processing module732). In particular, natural language processing is performed on the oneor more candidate text representations of block 1108 to determine theplurality of candidate user intents. Each candidate user intent of theplurality of candidate user intents is an actionable intent thatrepresents one or more tasks, which when performed, would satisfy apredicted goal corresponding to the user utterance. In some examples,each candidate user intent is determined in the form of a structuredquery.

In some examples, each candidate text representation of the one or morecandidate text representations of block 1108 is parsed to determine oneor more respective candidate user intents. In some examples, theplurality of candidate user intents determined at block 1112 includecandidate user intents corresponding to different candidate textrepresentations. For example, at block 1112, a first candidate userintent of the plurality of candidate user intents can be determined froma first candidate text representation of block 1108 and a secondcandidate user intent of the plurality of candidate user intents can bedetermined from a second candidate text representation of block 1108.

In some examples, each candidate user intent is associated with arespective intent confidence score. The intent confidence scores canindicate the confidence that a particular candidate user intent is thecorrect user intent for the respective candidate text representation. Inaddition, the intent confidence scores can indicate the confidence ofcorresponding domains, actionable intents, concepts, or propertiesdetermined for the candidate user intents. In some examples, theplurality of candidate user intents are the m-best candidate userintents having the m-highest intent confidence scores.

At block 1114, a plurality of candidate task flows are determined (e.g.,using task flow processing module 736 or 836) from the plurality ofcandidate user intents of block 1112. Specifically, each candidate userintent of the plurality of candidate user intents is mapped to acorresponding candidate task flow of the plurality of candidate taskflows. Each candidate task flow includes procedures for performing oneor more actions that fulfill the respective candidate user intent. Forcandidate user intents having incomplete structured queries (e.g.,partial structured queries with one or more missing property values),the corresponding candidate task flows can include procedures forresolving the incomplete structured queries. For example, the candidatetask flows can include procedures for determining one or more flowparameters (e.g., corresponding to the one or more missing propertyvalues) by searching one or more data sources or querying the user foradditional information. Each candidate task flow further includesprocedures for performing one or more actions represented by thecorresponding candidate user intent (e.g., represented by the completestructured query of the candidate user intent).

At block 1116, a plurality of task flow scores are determined (e.g.,using task flow processing module 736 or 836) for the plurality ofcandidate task flows. Each task flow score of the plurality of task flowscores corresponds to a respective candidate task flow of the pluralityof candidate task flows. The task flow score for a respective candidatetask flow can represent the likelihood that the respective candidatetask flow is the correct candidate task flow to perform given the userutterance. For example, the task flow score can represent the likelihoodthat the user's actual desired goal for providing the user utterance isfulfilled by performing the respective candidate task flow.

In some examples, each task flow score is based on a flow parameterscore for the respective candidate task flow. In these examples, block1116 includes determining (e.g., using task flow manager 838) arespective flow parameter score for each candidate task flow. The flowparameter score for a respective candidate task flow can represent aconfidence of resolving one or more flow parameters for the respectivecandidate task flow. In some examples, determining a flow parameterscore for a respective candidate task flow includes resolving one ormore flow parameters for the respective candidate task flow.Specifically, for each candidate task flow, process 1100 determines, atblock 1118, whether the respective candidate task flow includesprocedures for resolving one or more flow parameters. The one or moreflow parameters can correspond, for example, to one or more missingproperty values of a corresponding incomplete structured query. In someexamples, the one or more flow parameters are parameters that are notexpressly specified in the user utterance. If process 1100 determinesthat the respective candidate task flow includes procedures forresolving one or more flow parameters, the procedures can be executed(e.g., using task flow resolver 840) to resolve the one or more flowparameters. In some examples, executing the procedures causes one ormore data sources to be searched. In particular, the one or more datasources are searched to obtain one or more values for the one or moreflow parameters. In some examples, the one or more data sourcescorrespond to one or more properties of the respective candidate userintent.

If the one or more flow parameters for the respective candidate taskflow can be resolved (e.g., by successfully obtaining one or more valuesfor the one or more flow parameters from the one or more data sources),then the flow parameter score determined for the respective candidatetask flow can be high. Conversely, if the one or more flow parametersfor the respective candidate task flow cannot be resolved (e.g., due toa failure to obtain one or more values for the one or more flowparameters from the one or more data sources), then the flow parameterscore determined for the respective candidate task flow can be low.

Determining task flow scores and/or task parameter scores for theplurality of candidate task flows can be advantageous for evaluating thereliability of each candidate task flow prior to selecting and executingany candidate task flow. In particular, the task flow scores and/or taskparameter scores can be used to identify candidate task flows thatcannot be resolved. This allows process 1100 to only select (e.g., atblock 1122) and execute (e.g., at block 1124) candidate task flows thatcan be resolved, which improves the reliability and robustness of taskflow processing by the digital assistant.

In some examples, each task flow score of the plurality of task flowscores is based on the intent confidence score of a respective candidateuser intent corresponding to the respective candidate task flow.Further, in some examples, each task flow score of the plurality of taskflow scores is based on the speech recognition confidence score of therespective candidate text representation corresponding to the respectivecandidate task flow. In some examples, each task flow score is based ona combination of the flow parameter score for the respective candidatetask flow, the intent confidence score of the respective candidate userintent, and the speech recognition confidence score of the respectivecandidate text representation.

At block 1120, the plurality of candidate task flows are ranked (e.g.,using task flow manager 838) according to the plurality of task flowscores of block 1116. For example, the plurality of candidate task flowsare ranked from the highest task flow score to the lowest task flowscore.

At block 1122, a first candidate task flow of the plurality of candidatetask flows is selected (e.g., using task flow manager 838). Inparticular, the first candidate task flow of the plurality of candidatetask flows is selected based on the plurality of task flow scores andthe ranking of block 1120. For example, the selected first candidatetask flow is the highest ranked candidate task flow of the plurality ofcandidate task flows (e.g., having the highest task flow score).

In some examples, the selected first candidate task flow has the highesttask flow score, but corresponds to a candidate user intent having anintent confidence score that is not the highest intent confidence scoreamong the plurality of candidate user intents. In some examples, theselected first candidate task flow corresponds to a text representationhaving a speech recognition score that is not the highest speechrecognition score among the plurality of candidate text representations.

In the examples described above, process 1100 evaluates each of theplurality of candidate task flows in parallel to select the firstcandidate task flow having the highest task flow score. It should beappreciated, however, that in other examples, process 1100 can insteadevaluate the plurality of candidate task flows serially. For instance,in some examples, a first task flow score is initially determined onlyfor a candidate task flow corresponding to a candidate user intenthaving the highest intent confidence score. If the first task flow scoresatisfies a predetermined criterion (e.g., greater than a predeterminedthreshold level), then the corresponding candidate task flow is selectedat block 1122. If, however, the first task flow score does not satisfythe predetermined criterion (e.g., less than the predetermined thresholdlevel), then a second task flow score is determined for anothercandidate task flow corresponding to a candidate user intent having thenext highest intent confidence score. Depending on whether or not thesecond task flow score satisfies the predetermined criterion, theanother candidate task flow corresponding to the second task flow scorecan be selected at block 1122, or additional task flow scores can besubsequently determined for additional candidate task flows based on theassociated intent confidence scores.

Selecting the first candidate task flow based on the plurality of taskflow scores can enhance the accuracy and reliability of the digitalassistant on the electronic device. In particular, using the pluralityof task flow scores, process 1100 can avoid selecting candidate taskflows that cannot be resolved. This can reduce the likelihood of taskflow processing errors during execution of the selected first candidatetask flow. Moreover, because candidate task flows that cannot beresolved are less likely to coincide with the user's actual goals,selecting the first candidate task flow based on the plurality of taskflow scores can increase the likelihood that the selected firstcandidate task flow coincides with the user's actual desired goal. As aresult, the accuracy and reliability of the digital assistant on theelectronic device can be improved by selecting the first candidate taskflow based on the plurality of task flow scores.

At block 1124, the first candidate task flow selected at block 1122 isexecuted (e.g., using task flow manager 838). Specifically, one or moreactions represented by the first candidate task flow are performed. Insome examples, results are obtained by executing the first candidatetask flow. The results can include, for example, information requestedby the user in the user utterance. In some examples, not all actionsrepresented by the first candidate task flow are performed at block1124. Specifically, actions that provide an output to the user of thedevice are not performed at block 1124. For example, block 1124 does notinclude displaying, on a display of the electronic device, the resultsobtained by executing the first candidate task flow. Nor does block 1124include providing audio output (e.g., speech dialogue or music) on theelectronic device. Thus, in some examples, the first candidate task flowis executed without providing any output to the user prior to detectinga speech end-point condition at block 1128.

In some examples, executing the first candidate task flow at block 1124can include performing the operations of block 1126. At block 1126, atext dialogue that is responsive to the user utterance is generated(e.g., using task flow manager 838 in conjunction with dialogue flowprocessing module 734). In some examples, the generated text dialogueincludes results obtained from executing the first candidate task flow.In some examples, the text dialogue is generated at block 1126 withoutoutputting the text dialogue or a spoken representation of the textdialogue to the user. In some examples, block 1126 further includesadditional operations for generating a spoken representation of the textdialogue for output (e.g., operations of blocks 1202-1208 in process1200, described below with reference to FIG. 12). In these examples,block 1126 can include generating a plurality of speech attribute valuesfor the text dialogue. The plurality of speech attribute values provideinformation that can be used to generate the spoken representation ofthe text dialogue. In some examples, the plurality of speech attributevalues can include a first speech attribute value that specifies thetext dialogue (e.g., a representation of the text dialogue that can beused by a speech synthesis processing module to convert the textdialogue into corresponding speech). In some examples, the plurality ofspeech attribute values can specify one or more speech characteristicsfor generating the spoken representation of the text dialogue, such aslanguage, gender, audio quality, type (e.g., accent/localization),speech rate, volume, pitch, or the like.

At block 1128, a determination is made as to whether a speech end-pointcondition is detected between the second time (e.g., second time 908 or1008) and the third time (e.g., third time 910 or 1012). A speechend-point refers to a point in the stream of audio where the user hasfinished speaking (e.g., end of the user utterance). The determinationof block 1128 is made, for example, while the stream of audio is beingreceived from the second time to the third time at block 1102. In someexamples, the determination of block 1128 is performed by monitoring oneor more audio characteristics in the second portion of the stream ofaudio. For instance, in some examples, detecting the speech end-pointcondition can include detecting, in the second portion of the stream ofaudio, an absence of user speech for greater than a second predeterminedduration (e.g., 600 ms, 700 ms, or 800 ms). In these examples, block1128 includes determining whether the second portion of the stream ofaudio contains a continuation of the user utterance in the first portionof the stream of audio. If a continuation of the user utterance in thefirst portion of the stream of audio is detected in the second portionof the stream of audio, then process 1100 can determine that a speechend-point condition is not detected between the second time and thethird time. If a continuation of the user utterance in the first portionof the stream of audio is not detected in the second portion of thestream of audio for greater than the second predetermined duration,process 1100 can determine that a speech end-point condition is detectedbetween the second time and the third time. The absence of user speechcan be detected using similar speech detection techniques describedabove with respect to block 1110. In some examples, the secondpredetermined duration is longer than the first predetermined durationof block 1110.

In some examples, detecting the speech end-point condition includesdetecting a predetermined type of non-speech input from the user betweenthe second time and the third time. For example, a user may invoke thedigital assistant at the first time by pressing and holding a button(e.g., “home” or menu button 304) of the electronic device. In thisexample, the predetermined type of non-speech input can be the userreleasing the button (e.g., at the third time). In other examples, thepredetermined type of non-speech input is a user input of an affordancedisplayed on the touch screen (e.g., touch screen 212) of the electronicdevice.

In response to determining that a speech end-point condition is detectedbetween the second time and the third time, block 1130 is performed.Specifically, at block 1130, results from executing the selected firstcandidate task flow at block 1124 are presented to the user. In someexamples, block 1130 includes outputting the results on the electronicdevice to the user. For example, the results are displayed on a displayof the electronic device. The results can include, for example, the textdialogue generated at block 1126. In some examples, the results arepresented to the user in the form of audio output. For example, theresults can include music or speech dialogue.

In some examples, presenting the results at block 1130 includesperforming the operations of block 1132. Specifically, at block 1132, aspoken representation of the text dialogue generated at block 1126 isoutputted. Outputting the spoken representation of the text dialogueincludes, for example, playing an audio file having the spokenrepresentation of the text dialogue. In some examples, outputting thespoken representation of the text dialogue includes performing one ormore of the blocks of process 1200, described below with reference toFIG. 12. For example, outputting the spoken representation of the textdialogue includes determining whether the memory of the electronicdevice stores an audio file having the spoken representation of the textdialogue (block 1204). In response to determining that the memory of theelectronic device stores an audio file having the spoken representationof the text dialogue, the spoken representation of the text dialogue isoutputted by playing the stored audio file (block 1212). In response todetermining that the memory of the electronic device does not store anaudio file having the spoken representation of the text dialogue, anaudio file having the spoken representation of the text dialogue isgenerated (block 1206) and stored (block 1208) in the memory of theelectronic device. In response to determining that the speech end-pointcondition is detected (block 1128 or 1210), the stored audio file isplayed to output the spoken representation of the text dialogue (block1212).

As discussed above, at least partially performing the operations ofblocks 1112-1126 and/or 1202-1208 between the second time and the thirdtime (prior to detecting the speech end-point condition at block 1128 or1210) can reduce the number of operations required to be performed upondetecting the speech end-point condition. Thus, less computation can berequired upon detecting the speech end-point condition, which can enablethe digital assistant to provide a quicker response (e.g., by presentingthe results at block 1130 or outputting spoken dialogue at block 1132 or1212) upon detecting the speech end-point condition.

With reference back to block 1128, in response to determining that aspeech end-point condition is not detected between the second time andthe third time, process 1100 forgoes performance of block 1130 (andblock 1132). For example, if process 1100 determines that the secondportion of the stream of audio contains a continuation of the userutterance, then no speech end-point condition is detected between thesecond time and the third time and process 1100 forgoes performance ofblocks 1130 and 1132. Specifically, process 1100 forgoes presentingresults from executing the selected first candidate task flow of block1122. In examples where text dialogue is generated, process 1100 furtherforgoes output of a spoken representation of the text dialogue.Furthermore, if process 1100 is still performing any of the operationsof blocks 1112-1126 or blocks 1202-1208 with respect to the utterance inthe first portion of the stream of audio, process 1100 ceases to performthese operations upon determining that a speech end-point condition isnot detected between the second time and the third time.

In some examples, in response to determining that a speech end-pointcondition is not detected between the second time and the third time,process 1100 can return to one or more of blocks 1102-1126 to processthe speech in the second portion of the stream of audio. Specifically,upon detecting a continuation of the user utterance in the secondportion of the stream of audio, speech recognition is performed (block1108) on the continuation of the user utterance in the second portion ofthe stream of audio. Additionally, in some examples, process 1100continues to receive the stream of audio (block 1102) after the thirdtime. Specifically, a third portion of the stream of audio can bereceived (block 1102) from the third time (third time 1012) to a fourthtime (fourth time 1014).

In some examples, the speech recognition results of the continuation ofthe user utterance in the second portion of the stream of audio iscombined with the speech recognition results of the user utterance inthe first portion of the stream of audio to obtain a second plurality ofcandidate text representations. Each candidate text representation ofthe second plurality of candidate text representations is a textrepresentation of the user utterance across the first and secondportions of the stream of audio.

A determination is made (block 1110) as to whether the second portion ofthe stream of audio satisfies a predetermined condition. In response todetermining that the second portion of the stream of audio satisfies apredetermined condition, one or more of the operations of blocks1112-1126 are performed with respect to the second plurality ofcandidate text representations. In particular, in response todetermining that the second portion of the stream of audio satisfies apredetermined condition, one or more of the operations of blocks1112-1130 are at least partially performed between the third time (e.g.,third time 1012) and the fourth time (e.g., fourth time 1014) (e.g.,while receiving the third portion of the stream of audio at block 1102).

Based on the second plurality of candidate text representations, asecond plurality of candidate user intents for the user utterance in thefirst and second portions of the stream of audio are determined (block1112). A second plurality of candidate task flows are determined (block1114) from the second plurality of candidate user intents. Specifically,each candidate user intent of the second plurality of candidate userintents is mapped to a corresponding candidate task flow of the secondplurality of candidate task flows. A second candidate task flow isselected from the second plurality of candidate task flows (block 1122).The selection can be based on a second plurality of task flow scoresdetermined for the second plurality of candidate task flows (block1116). The selected second candidate task flow is executed (block 1124)without providing any output to the user prior to detecting a speechend-point condition. In some examples, second results are obtained fromexecuting the second candidate task flow. In some examples, executingthe second candidate task flow includes generating a second textdialogue (block 1126) that is responsive to the user utterance in thefirst and second portions of the stream of audio. In some examples, thesecond text dialogue is generated without outputting the second textdialogue or a spoken representation of the second text dialogue to theuser prior to detecting a speech end-point condition. In some examples,additional operations for generating a spoken representation of thesecond text dialogue for output are performed (e.g., operations inblocks 1202-1208 of process 1200, described below with reference to FIG.12).

In some examples, a determination is made (block 1128) as to whether aspeech end-point condition is detected between the third time and thefourth time. In response to determining that a speech end-pointcondition is detected between the third time and the fourth time, secondresults from executing the selected second candidate task flow arepresented to the user (block 1130). In some examples, presenting thesecond results includes outputting, to the user of the device, thespoken representation of the second text dialogue by playing a storedsecond audio file (e.g., a stored second audio file generated at block1206).

FIG. 12 illustrates process 1200 for operating a digital assistant togenerate a spoken dialogue response, according to various examples. Insome examples, process 1200 is implemented as part of process 1100 foroperating a digital assistant. Process 1100 is performed, for example,using one or more electronic devices implementing a digital assistant.Implementing process 1200 in a digital assistant system can reduce thelatency associated with text-to-speech processing. In some examples,process 1200 is performed using a client-server system (e.g., system100), and the blocks of process 1200 are divided up in any mannerbetween the server (e.g., DA server 106) and a client device (userdevice 104). In some examples, process 1200 is performed using only aclient device (e.g., user device 104) or only multiple client devices.In process 1100, some blocks are, optionally, combined, the order ofsome blocks is, optionally, changed, and some blocks are, optionally,omitted. In some examples, additional operations may be performed incombination with process 1200.

At block 1202, a text dialogue is received (e.g., at speech synthesisprocessing module 740). In some examples, the text dialogue is generatedby the digital assistant system (e.g., at block 1126) in response to areceived user utterance (e.g., at block 1102). In some examples, thetext dialogue is received with a plurality of associated speechattribute values (e.g., speech attribute values, described above withreference to block 1126). In some examples, the plurality of speechattribute values specify one or more speech characteristics forgenerating the spoken representation of the text dialogue. The one ormore speech characteristics include, for example, language, gender,audio quality, type (e.g., accent/localization), speech rate, volume,pitch, or the like. The combination of the text dialogue and theplurality of speech attribute values can represent a request to generatea spoken representation of the text dialogue in accordance with the oneor more speech characteristics defined in the plurality of speechattribute values.

In response to receiving the text dialogue at block 1202, block 1204 isperformed. At block 1204, a determination is made (e.g., using speechsynthesis processing module 740) as to whether the memory (e.g., memory202, 470, or 702) of the electronic device (e.g., device 104, 200, 600,or 700) stores an audio file having the spoken representation of thetext dialogue. For example, block 1204 includes searching the memory ofthe electronic device for an audio file having the spoken representationof the text dialogue. In some examples, the memory of the electronicdevice contains one or more audio files. In these examples, block 1204includes analyzing each audio file of the one or more audio files todetermine whether one of the one or more audio files includes aplurality of speech attribute values that match the plurality of speechattribute values for the text dialogue received at block 1202. If anaudio file of the one or more audio files has a first plurality ofspeech attribute values that match the plurality of speech attributevalues for the text dialogue, then it would be determined that thememory stores an audio file having the spoken representation of the textdialogue.

In some examples, block 1204 includes searching the file names of theone or more audio files stored in the memory of the electronic device.In these examples, the file name of each audio file is analyzed todetermine whether the file name represents a plurality of speechattribute values that match the plurality of speech attribute values forthe text dialogue. Specifically, each file name can encode (e.g., usingmd5 hash) a plurality of speech attribute values. Thus, analyzing thefile names of the one or more audio files stored in the memory candetermine whether the memory stores an audio file having the spokenrepresentation of the text dialogue.

In response to determining that the memory of the electronic devicestores an audio file having the spoken representation of the textdialogue, process 1200 forgoes performance of blocks 1206 and 1208 andproceeds to block 1210. In response to determining that the memory ofthe electronic device does not store an audio file having the spokenrepresentation of the text dialogue, block 1206 is performed.

At block 1206, an audio file having the spoken representation of thetext dialogue is generated (e.g., using speech synthesis processingmodule 740). In particular, speech synthesis is performed using the textdialogue and the associated plurality of speech attribute values togenerate the audio file of the spoken representation of the textdialogue. The spoken representation of the text dialogue is generatedaccording to the one or more speech characteristics specified in theplurality of speech attribute values.

At block 1208, the audio file generated at block 1206 is stored in thememory of the electronic device. In some examples, the audio file havingthe spoken representation of the text dialogue can indicate theplurality of speech attribute values for the text dialogue.Specifically, in some examples, the audio file having the spokenrepresentation of the text dialogue is stored with a file name thatencodes the plurality of speech attribute values for the text dialogue(e.g., using md5 hash).

In some examples, blocks 1202-1208 are performed without providing anyoutput (e.g., audio or visual) to the user. Specifically, neither thetext dialogue nor the spoken representation of the text dialogue isoutputted to the user prior to determining that the speech end-pointcondition is detected at block 1210. Blocks 1202-1208 of process 1200are performed at least partially prior to a speech end-point conditionbeing detected at block 1210. This can be advantageous for reducing theresponse latency of the digital assistant on the electronic device.

At block 1210, a determination is made (e.g., using latency managementmodule 780) as to whether a speech end-point condition is detected.Block 1208 is similar or substantially identical to block 1128,described above. For example, the determination can be made between thesecond time (e.g., second time 908) and the third time (e.g., third time910) while the second portion of the stream of audio is received atblock 1102. In response to determining that a speech end-point conditionis detected, block 1212 is performed. Specifically, at block 1212, thespoken representation of the text dialogue is outputted to the user byplaying the stored audio file. Block 1212 is similar or substantiallyidentical to block 1132. In response to determining that a speechend-point condition is not detected, process 1200 forgoes output of thespoken representation of the text dialogue (block 1214). For example,process 1100 can remain at block 1210 to await detection of the speechend-point condition.

In the examples described above, a suitable candidate task flow is firstselected (block 1122) and the selected candidate task flow is thenexecuted (block 1124). Moreover, an audio file of spoken dialogue isgenerated (block 1206) only for the selected candidate task flow.However, it should be recognized that, in other examples, a suitablecandidate task flow can be selected at block 1122 while executing aplurality of candidate task flows at block 1124. In certainimplementations, executing the plurality of candidate task flows priorto selecting a suitable candidate task flow can be advantageous forreducing latency. Specifically, determining the task flow scores (block1116) and selecting a suitable candidate task flow (block 1122) based onthe determined task flow scores can be computationally intensive andthus to reduce latency, the plurality of candidate task flows can beexecuted in parallel while determining the task flow scores andselecting a suitable candidate task flow. In addition, a plurality ofrespective audio files containing spoken dialogues for the plurality ofcandidate task flows can be generated at block 1206 while determiningthe task flow scores and selecting a suitable candidate task flow. Byperforming these operations in parallel, when a suitable candidate taskflow is selected, the selected candidate task flow would have been, forexample, at least partially executed and the respective audio filecontaining spoken dialogue for the selected candidate task flow wouldhave been, for example, at least partially generated. As a result,response latency can be further reduced. Upon detecting a speechend-point condition at block 1128 or 1210, the result corresponding tothe selected candidate task flow can be retrieved from the plurality ofresults and presented to the user. In addition, the audio filecorresponding to the selected candidate task flow can be retrieved fromthe plurality of audio files and played to output the correspondingspoken dialogue for the selected candidate task flow.

The operations described above with reference to FIGS. 11A-11B and 12are optionally implemented by components depicted in FIGS. 1-4, 6A-B,7A-7C, and 8. For example, the operations of processes 1100 and 1200 maybe implemented by I/O processing module 728, STT processing module 730,natural language processing module 732, dialogue flow processing module734, task flow processing module 736, speech synthesis processing module740, audio processing module 770, latency management module 780, taskflow manager 838, and task flow resolver 840. It would be clear to aperson having ordinary skill in the art how other processes areimplemented based on the components depicted in FIGS. 1-4, 6A-B, 7A-7C,and 8.

In accordance with some implementations, a computer-readable storagemedium (e.g., a non-transitory computer-readable storage medium) isprovided, the computer-readable storage medium storing one or moreprograms for execution by one or more processors of an electronicdevice, the one or more programs including instructions for performingany of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises means forperforming any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises a processing unitconfigured to perform any of the methods or processes described herein.

In accordance with some implementations, an electronic device (e.g., aportable electronic device) is provided that comprises one or moreprocessors and memory storing one or more programs for execution by theone or more processors, the one or more programs including instructionsfor performing any of the methods or processes described herein.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the techniques and their practical applications. Othersskilled in the art are thereby enabled to best utilize the techniquesand various embodiments with various modifications as are suited to theparticular use contemplated.

Although the disclosure and examples have been fully described withreference to the accompanying drawings, it is to be noted that variouschanges and modifications will become apparent to those skilled in theart. Such changes and modifications are to be understood as beingincluded within the scope of the disclosure and examples as defined bythe claims.

What is claimed is:
 1. An electronic device, comprising: one or moreprocessors; and memory storing one or more programs configured to beexecuted by the one or more processors, the one or more programsincluding instructions for: receiving a user utterance; determining,based on a plurality of candidate text representations of the userutterance, a plurality of candidate user intents for the user utterance,wherein each candidate user intent of the plurality of candidate userintents corresponds to a respective candidate task flow of a pluralityof candidate task flows; determining a plurality of task flow scores forthe plurality of candidate task flows, each task flow score of theplurality of task flow scores corresponding to a respective candidatetask flow of the plurality of candidate task flows; selecting, based onthe plurality of task flow scores, a first candidate task flow of theplurality of candidate task flows; and executing the first candidatetask flow, including presenting, to the user, results from executing thefirst candidate task flow.
 2. The electronic device of claim 1, whereinthe one or more programs include further instructions for: for eachcandidate task flow of the plurality of candidate task flows: resolvingone or more flow parameters of the respective candidate task flow,wherein a respective task flow score for the respective candidate taskflow is based on resolving the one or more flow parameters of therespective candidate task flow.
 3. The electronic device of claim 2,wherein resolving the one or more flow parameters of the respectivecandidate task flow comprises searching a data source for one or morevalues corresponding to the one or more flow parameters, the data sourcecorresponding to one or more properties of a respective candidate userintent of the plurality of candidate user intents.
 4. The electronicdevice of claim 1, wherein: each candidate text representation of theplurality of candidate text representations has an associated speechrecognition confidence score; and each task flow score of the pluralityof task flow scores is based on a respective speech recognitionconfidence score of a respective candidate text representation of theplurality of candidate text representations.
 5. The electronic device ofclaim 1, wherein: each candidate user intent of the plurality ofcandidate user intents has an associated intent confidence score; andeach task flow score of the plurality of task flow scores is based on arespective intent confidence score of a respective candidate user intentof the plurality of candidate user intents.
 6. The electronic device ofclaim 1, wherein: a first candidate user intent of the plurality ofcandidate user intents is determined from a first candidate textrepresentation of the plurality of candidate text representations; and asecond candidate user intent of the plurality of candidate user intentsis determined from a second candidate text representation of theplurality of candidate text representations.
 7. The electronic device ofclaim 1, wherein the one or more programs include further instructionsfor: ranking the plurality of candidate task flows in accordance withthe plurality of task flow scores, wherein selecting the first candidatetask flow is based on the ranking of the plurality of candidate taskflows.
 8. A method for operating a digital assistant, the methodcomprising: at an electronic device having one or more processors andmemory: receiving a user utterance; determining, based on a plurality ofcandidate text representations of the user utterance, a plurality ofcandidate user intents for the user utterance, wherein each candidateuser intent of the plurality of candidate user intents corresponds to arespective candidate task flow of a plurality of candidate task flows;determining a plurality of task flow scores for the plurality ofcandidate task flows, each task flow score of the plurality of task flowscores corresponding to a respective candidate task flow of theplurality of candidate task flows; selecting, based on the plurality oftask flow scores, a first candidate task flow of the plurality ofcandidate task flows; and executing the first candidate task flow,including presenting, to the user, results from executing the firstcandidate task flow.
 9. The method of claim 8, further comprising: foreach candidate task flow of the plurality of candidate task flows:resolving one or more flow parameters of the respective candidate taskflow, wherein a respective task flow score for the respective candidatetask flow is based on resolving the one or more flow parameters of therespective candidate task flow.
 10. The method of claim 9, whereinresolving the one or more flow parameters of the respective candidatetask flow comprises searching a data source for one or more valuescorresponding to the one or more flow parameters, the data sourcecorresponding to one or more properties of a respective candidate userintent of the plurality of candidate user intents.
 11. The method ofclaim 8, wherein: each candidate text representation of the plurality ofcandidate text representations has an associated speech recognitionconfidence score; and each task flow score of the plurality of task flowscores is based on a respective speech recognition confidence score of arespective candidate text representation of the plurality of candidatetext representations.
 12. The method of claim 8, wherein: each candidateuser intent of the plurality of candidate user intents has an associatedintent confidence score; and each task flow score of the plurality oftask flow scores is based on a respective intent confidence score of arespective candidate user intent of the plurality of candidate userintents.
 13. The method of claim 8, wherein: a first candidate userintent of the plurality of candidate user intents is determined from afirst candidate text representation of the plurality of candidate textrepresentations; and a second candidate user intent of the plurality ofcandidate user intents is determined from a second candidate textrepresentation of the plurality of candidate text representations. 14.The method of claim 8, further comprising: ranking the plurality ofcandidate task flows in accordance with the plurality of task flowscores, wherein selecting the first candidate task flow is based on theranking of the plurality of candidate task flows.
 15. A non-transitorycomputer-readable storage medium storing one or more programs configuredto be executed by one or more processors of an electronic device, theone or more programs including instructions for: receiving a userutterance; determining, based on a plurality of candidate textrepresentations of the user utterance, a plurality of candidate userintents for the user utterance, wherein each candidate user intent ofthe plurality of candidate user intents corresponds to a respectivecandidate task flow of a plurality of candidate task flows; determininga plurality of task flow scores for the plurality of candidate taskflows, each task flow score of the plurality of task flow scorescorresponding to a respective candidate task flow of the plurality ofcandidate task flows; selecting, based on the plurality of task flowscores, a first candidate task flow of the plurality of candidate taskflows; and executing the first candidate task flow, includingpresenting, to the user, results from executing the first candidate taskflow.
 16. The non-transitory computer-readable storage medium of claim15, wherein the one or more programs further include instructions for:for each candidate task flow of the plurality of candidate task flows:resolving one or more flow parameters of the respective candidate taskflow, wherein a respective task flow score for the respective candidatetask flow is based on resolving the one or more flow parameters of therespective candidate task flow.
 17. The non-transitory computer-readablestorage medium of claim 16, wherein resolving the one or more flowparameters of the respective candidate task flow comprises searching adata source for one or more values corresponding to the one or more flowparameters, the data source corresponding to one or more properties of arespective candidate user intent of the plurality of candidate userintents.
 18. The non-transitory computer-readable storage medium ofclaim 15, wherein: each candidate text representation of the pluralityof candidate text representations has an associated speech recognitionconfidence score; and each task flow score of the plurality of task flowscores is based on a respective speech recognition confidence score of arespective candidate text representation of the plurality of candidatetext representations.
 19. The non-transitory computer-readable storagemedium of claim 15, wherein: each candidate user intent of the pluralityof candidate user intents has an associated intent confidence score; andeach task flow score of the plurality of task flow scores is based on arespective intent confidence score of a respective candidate user intentof the plurality of candidate user intents.
 20. The non-transitorycomputer-readable storage medium of claim 15, wherein: a first candidateuser intent of the plurality of candidate user intents is determinedfrom a first candidate text representation of the plurality of candidatetext representations; and a second candidate user intent of theplurality of candidate user intents is determined from a secondcandidate text representation of the plurality of candidate textrepresentations.
 21. The non-transitory computer-readable storage mediumof claim 15, wherein the one or more programs further includeinstructions for: ranking the plurality of candidate task flows inaccordance with the plurality of task flow scores, wherein selecting thefirst candidate task flow is based on the ranking of the plurality ofcandidate task flows.
 22. An electronic device, comprising: one or moreprocessors; and memory storing one or more programs configured to beexecuted by the one or more processors, the one or more programsincluding instructions for: receiving a user utterance; determining,based on a plurality of candidate text representations of the userutterance, a plurality of candidate user intents for the user utterance,wherein each candidate user intent of the plurality of candidate userintents corresponds to a respective candidate task flow of a pluralityof candidate task flows, and wherein a first candidate task flow of theplurality of candidate task flows corresponds to a first candidate userintent of the plurality of candidate user intents; determining aplurality of task flow scores for the plurality of candidate task flows,each task flow score of the plurality of task flow scores correspondingto a respective candidate task flow of the plurality of candidate taskflows, wherein: a first task flow score of the plurality of task flowscores is for the first candidate task flow; determining the pluralityof task flow scores includes determining a flow parameter valuecorresponding to a property of the first candidate user intent, the flowparameter value not specified in the user utterance; and the first taskflow score is based on whether the flow parameter value can bedetermined by the electronic device; selecting, based on the pluralityof task flow scores, the first candidate task flow; and executing thefirst candidate task flow, including presenting, to the user, resultsfrom executing the first candidate task flow.
 23. The electronic deviceof claim 22, wherein the first task flow score is further based on afirst intent confidence score of the first candidate user intent. 24.The electronic device of claim 23, wherein the first task flow score isa highest task flow score of the plurality of task flow scores, andwherein the first intent confidence score is not a highest intentconfidence score of a plurality of intent confidence scores thatcorrespond to the plurality of candidate user intents.
 25. Theelectronic device of claim 22, wherein: the first candidate user intentis determined from a first candidate text representation of theplurality of candidate text representations; and the first task flowscore is further based on a first speech recognition confidence score ofthe first candidate text representation.
 26. The electronic device ofclaim 25, wherein the first task flow score is a highest task flow scoreof the plurality of task flow scores, and wherein the first speechrecognition confidence score is not a highest speech recognitionconfidence score of a plurality of speech recognition confidence scoresthat correspond to the plurality of candidate text representations.